Atlassian Intelligence and Rovo are designed for transparency
Our no b.s. commitment to open communication, accountability, and helping teams to use AI responsibly.
Rovo
Select a Rovo feature below to get a transparent look at use cases and data use.
| Rovo Agents are powered by large language models developed by OpenAI and Google, as well as a combination of open-source large language models (including the Llama series) and other machine learning models. These models include OpenAI’s GPT series of models and Google’s Gemini series of models. Rovo Agents use these models to provide functionalities to analyze and generate responses to prompts in natural language, and provide relevant responses from Atlassian and connected third-party products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. For more information on open-source language models, see information on the Llama series. |
| With Rovo, we provide a number of out-of-the-box Agents, ready to use for a variety of tasks like, helping with decisionmaking, publishing knowledge documentation, and tidying up or organizing work items in Jira. Agents are specialized AI teammates that can assist human teams with moving work forward quickly and effectively. You can:
We believe that Rovo Agents work best in scenarios where:
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| It’s important to remember that because of the way that the models used to power Rovo Agents work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Rovo Agents are less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how Rovo Agents use your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, Rovo Agents apply the following measures.
The details above apply to Agents that are provided by Atlassian. For more information about Agents provided by Atlassian, please see Rovo data, privacy and usage guidelines | Rovo | Atlassian Support. For information about Agents provided by third parties, please reference the Vendor Terms provided by those third parties for their apps. Read more about Atlassian Intelligence |
| Rovo Chat is powered by large language models developed by OpenAI and Google, as well as a combination of open-source large language models (including the Llama series) and other machine learning models. These models include OpenAI’s GPT series of models and Google’s Gemini series of models. Rovo Chat uses these models to analyze and generate responses to prompts using natural language, and provide relevant responses from Atlassian and connected third-party products. Responses are generated by these large language models based on your inputs and are probabilistic in nature. That means that responses from large language models are generated by predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. For more information on open-source language models, see information on the Llama series. |
| Rovo Chat allows allows you to access the knowledge of your organization through a conversational interface. This means you can ask Rovo to write, read, review, or create things the same way you might ask a person, without disrupting your workflow. Chat understands the context of your work (both within Atlassian and your connected third-party products) to answer questions, or help provide ideas or insights. We believe that Rovo Chat works best in scenarios where:
Rovo Chat can also be accessed by users via a Chrome browser extension. Learn more about accessing Rovo Chat here and here. |
| It’s important to remember that because of the way that the models used to power Rovo Chat work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Rovo Chat is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Rovo Chat and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how Rovo Chat uses your data. This section supplements the information available on our Trust Center. We process: Your prompts (inputs) and responses (outputs).
When it comes to your data, Rovo Chat applies the following measures.
OpenAI is a subprocessor on our List of Subprocessors. They do not use your inputs and outputs for any purpose besides processing or acting on your request. This feature follows the permissions in your instance. For example, if you do not have access to a certain Confluence page, you will not be suggested content from that page in the response you receive. If you do not want your content to be available in responses to other users in your instance, please work with your org admin to ensure your permissions are set appropriately. When you access Rovo Chat from the Chrome browser extension, Chat will only read the content on the web page you are viewing to complement the content already within your Atlassian products. No additional data from the web page will be stored. Read more about Atlassian Intelligence Discover more about using Atlassian Intelligence |
| Rovo Search uses AI to enhance search capabilities across Atlassian and third-party tools. Rovo Search is powered by large language models developed by OpenAI and Google, as well as a combination of open-source large language models (including the Llama series) and other machine learning models. These models include OpenAI’s GPT series of models and Google’s Gemini series of models. Rovo Search uses these models to provide functionalities like semantic search, relevance ranking, and natural language processing. This includes analyzing and generating responses to search queries in natural language, and providing relevant responses from Atlassian and connected third-party products. Responses are generated by these large language models based on your inputs and are probabilistic in nature. That means that responses from large language models are generated by predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. For more information on open-source language models, see information on the Llama series. |
| Rovo Search enables users to search across multiple tools and platforms, including Atlassian products and connected third-party products, providing contextual and relevant results to enhance team collaboration and productivity. We believe that Rovo Search works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power Rovo Search work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Rovo Search is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Rovo and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how Rovo Search uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, Rovo Search applies the following measures.
Read more about Atlassian Intelligence What is Atlassian Intelligence? | Atlassian Support Rovo: Unlock organizational knowledge with GenAI | Atlassian |
Accelerate work with AI
Select an Atlassian Intelligence feature below to get a transparent look at use cases and data use.
- AI ChatOps for incident management
- AI drafts
- AI Summaries in Company Hub Cards
- AI related resources
- AI suggestions
- Automation
- Alert grouping
- Confluence quick summary
- Define terms
- Generative AI in the editor
- Work item reformatter
- Summarize work item details
- Summarize Smart Links
- Virtual service agent
- Summarize work item details using Atlassian Intelligence
- AI Work Breakdown
- AI ChatOps for incident management
- AI drafts
- AI Summaries in Company Hub Cards
- AI related resources
- AI suggestions
- Automation
- Alert grouping
- Confluence quick summary
- Define terms
- Generative AI in the editor
- Work item reformatter
- Summarize work item details
- Summarize Smart Links
- Virtual service agent
- Summarize work item details using Atlassian Intelligence
- AI Work Breakdown
| The AI ChatOps for incident management features are powered by large language models developed by OpenAI, as well as a combination of open-source large language models (including the Llama series and Phi series) and other machine learning models. These large language models include OpenAI’s GPT series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These large language models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models or about this approach in OpenAI’s research papers. For more information on open-source language models, see information on the Llama series and the Phi series. |
| AI ChatOps for incident management help your users to expedite the incident resolution process by offering a summary of the relevant incident and all the conversations about it so far to new users when they’re added to the Slack channel which is linked to the incident work item in Jira Service Management. Additionally, AI ChatOps for incident management can log the conversations that occur in Slack to Jira Service Management as a timeline for future reference. We believe that AI ChatOps for incident management work best in scenarios where:
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| Remember that because of the way that the models used to power AI ChatOps for incident management work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they’re based on or include content that sounds reasonable but is false or incomplete. We’ve found that AI ChatOps for incident management are less useful in scenarios where:
For this reason, we encourage you to consider situations where you use AI ChatOps for incident management and review the quality of the responses you receive before sharing them with others. You might also want to think about reviewing permissions to ensure that users have appropriate levels of access to relevant incident work items and Slack channels. |
| We understand you may have questions about how AI ChatOps for incident management uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, AI ChatOps for incident management apply the following measures:
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| AI drafts is powered by large language models developed by OpenAI and Anthropic, as well as a combination of open-source transformer-based language models and other machine learning models. These large language models include OpenAI’s GPT series of models and Anthropic’s Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. The open-source encoder models convert your textual inputs into numerical forms (embeddings) which are used for identifying and forming topics from your inputs. These large language models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Anthropic’s models. For more information on open-source language models, see information on embedding models. |
| AI drafts suggests pre-generated drafts of knowledge articles for your admins and agents to consider. This feature generates drafts for the most common work items in a Jira Service Management project, using the details and comments within work items in that project. This allows your service teams to increase the coverage of your knowledge base articles more quickly and easily, which will, in turn, benefit the performance of other features in Jira Service Management. For example, knowledge articles created using AI drafts can then be used by Virtual Service Agent’s AI answers feature to resolve requests from your help seekers more easily and quickly. Read more about AI answers in virtual service agent. We believe that AI drafts work best in scenarios where:
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| It’s important to remember that because of the way that the models used to power AI drafts work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that AI drafts are less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how AI drafts use your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, AI drafts apply the following measures.
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| AI Summaries in Company Hub Cards is powered by large language models developed by OpenAI. These large language models include OpenAI’s GPT series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models. |
| AI summaries in Company Hub Cards helps you to quickly populate and publish your Company Hub, by suggesting AI-generated descriptions for cards. This feature uses Atlassian Intelligence to generate a summary of the linked Confluence page or blog post for your Company Hub card. We believe that AI Summaries in Company Hub Cards works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power AI Summaries in Company Hub Cards work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that AI Summaries in Company Hub Cards is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
| We understand you may have questions about how AI Summaries in Company Hub Cards uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, AI Summaries in Company Hub Cards applies the following measures.
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| AI related resources is powered by large language models developed by OpenAI, as well as a combination of open-source large language models (including the Llama series and Phi series) and other machine learning models. These large language models include OpenAI’s GPT series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products, and to provide relevant responses from Atlassian and connected third-party products. These large language models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models or about this approach in OpenAI's research papers. For more information on open-source language models, see information on the Llama series and the Phi series. |
| Atlassian Intelligence enables your users to speed up the process of resolving incidents by suggesting a list of resources that they can refer to, across your linked knowledge base spaces and articles, Jira work items, and (if you are a Rovo customer) any third-party products you have integrated through Rovo. Read more about Rovo and third party tools. We believe that AI related resources work best in scenarios where:
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| Remember that because of the way that the models used to power AI related resources work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. We’ve found that AI related resources is less useful in scenarios where:
For this reason, we encourage you to consider situations where you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about the following:
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| We understand you may have questions about how AI related resources uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, AI related resources applies the following measures:
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| AI suggestions in Jira Service Management is powered by large language models developed by OpenAI, and other machine learning models. These large language models include OpenAI’s GPT series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These large language models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models. |
| With AI suggestions in Jira Service Management, your team can quickly get up to speed by gathering important context about your service requests and incidents at a glance. Atlassian Intelligence helps your team to:
AI suggestions in Jira Service Management can also recommend that agents escalate a request or incident when the applicable SLA is about to be breached. In the case of service requests, this feature may also suggest that agents escalate that request where the models used to power these suggestions identify, based on the text of the reporter’s comments, a sense of urgency or anger with that request. We believe that AI suggestions in Jira Service Management work best in scenarios where:
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| It’s important to remember that because of the way that the models used to power AI suggestions in Jira Service Management work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. We’ve found that AI suggestions in Jira Service Management are less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how AI suggestions in Jira Service Management uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, AI suggestions apply the following measures.
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| Automation using Atlassian Intelligence is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze natural language input and generate an automation rule for you within Jira and Confluence. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| Creating automation rules is at the core of the everyday automation experience, and we want to make this even easier for you by adding Atlassian Intelligence to the automation rule builder in Jira and Confluence. Now, you can easily create automation rules by simply typing in and describing what you wish to automate, and let Atlassian Intelligence handle all the heavy lifting of creating the rule for you. Find out more about Automation using Atlassian Intelligence for Jira and for Confluence. We believe that Automation using Atlassian Intelligence for Jira and Confluence works best in scenarios when you are not sure how to get started or want to accelerate the rule creation process. Not sure how best to create an automation rule?Automation rules are created by a combination of different types of components: triggers, actions, conditions, and branches. Think of components as the building blocks of a rule. To successfully create a rule with Atlassian Intelligence, your rule must at least contain both a trigger and an action. For example: In Jira: Every Monday, find all the tasks with a due date in the next 7 days, and send the assignee a reminder email. When a ticket moves to Testing, assign the ticket to John Smith. In Confluence:
In addition, for a rule to be successfully created, all its components must be supported by Automation using Atlassian Intelligence. This means that any triggers, actions, conditions, or branches in your rule must be compatible with Automation in Jira and/or Confluence. |
| It’s important to remember that because of the way that the models used to power Automation using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Automation using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. Automation using Atlassian Intelligence will only work with the existing set of available automation components in Jira and Confluence. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do, as described above. |
| We understand you may have questions about how Automation using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, using Atlassian Intelligence for Confluence automation applies the following measures:
All third-party LLM providers are subprocessors and listed as so on our Subprocessors page. They do not use your inputs and outputs for any purpose besides processing your request. This feature follows the permissions in your instance. For example, if you do not have access to a specific project or page, you will not be suggested content from those assets in the response you receive. If you do not want your content to be available in responses to other users in your instance, work with your org admin to ensure your permissions are set appropriately. |
| Alert grouping by Atlassian Intelligence is powered by large language models developed by OpenAI and Google. These models include an algorithm designed to identify patterns in alert data, and OpenAI's GPT series of models and Google's Gemini series of models. Atlassian Intelligence uses these machine learning models to analyze and generate alert groups and give related suggestions (past alert groups and past alert responders) within our products based on the similarity of the alert content or the tags used. Atlassian Intelligence then uses large language models to analyze and generate natural language descriptions and content for these groups within our products. These large language models generate responses based on your inputs and are probabilistic. This means that their responses are generated by predicting the most probable next word or text based on the data they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. |
| Alert grouping uses Atlassian Intelligence to identify and group similar alerts together. It also helps you by identifying and recommending past similar alert groups and past alert responders (or teams of responders), based on the semantic similarity of the alert content or tags used. When you want to escalate the alert group to an incident, alert grouping will also pre-populate all contextual information for you to review as part of the incident creation process. We believe that alert grouping works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power alert grouping work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. In the case of the alert groups that you see, they might not precisely reflect the semantic similarity of their tags. We’ve found that alert grouping is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about ensuring that you and your team follow consistent practices in using alert tags. |
| We understand you may have questions about how alert grouping uses your data. This section supplements the information available on our FAQ page. We process:
We process your alert data to train a version of the machine learning model to recognize patterns specific to your alerts. This version is used to serve only your experience:
When it comes to your data, alert grouping applies the following measures:
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| Summarize pages and blogs using Atlassian Intelligence is powered by large language models developed by developed by OpenAI and Google. These large language models include OpenAI's GPT series of models and Google's Gemini series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. |
| Save time and get the details you need to do your work faster by generating a quick summary of a Confluence page or blog with Atlassian Intelligence. Find out more about using Atlassian Intelligence in Confluence. We believe that summarizing pages and blogs using Atlassian Intelligence works best in scenarios when:
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| It’s important to remember that because of the way that the models used to power summarizing pages and blogs using Atlassian Intelligence work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. While we continue to build better support for macros, tables, and expand in summaries, we’ve found that summarizing pages and blogs using Atlassian Intelligence is less useful in scenarios where:
We encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how using Atlassian Intelligence for Confluence automation uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, summarizing pages and blogs using Atlassian Intelligence applies the following measures:
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| Defining terms using Atlassian Intelligence in Confluence and Jira is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language answers within Confluence and Jira. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| One of the most challenging things about consuming content in Confluence and Jira can be getting the context you need to understand what you’re reading. Abbreviations, acronyms, unfamiliar terms, and team or project-specific names can lead to a lengthy search for the information you need. Defining terms using Atlassian Intelligence will provide the definition of company-specific terms (such as acronyms, project, system, or team names) on a page in Confluence or in a work item description in Jira. This gives users the information they need, when they need it - all whilst helping teams work better together. Atlassian Intelligence can save you time by defining these things for you, without navigating away from what you’re reading. If you encounter a definition that you feel is inaccurate, you can edit or add a new definition, then set the visibility to be for that page or work item, the whole space or project, or access your entire organization. We believe that defining terms using Atlassian Intelligence in Confluence and Jira works best in scenarios when:
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| It’s important to remember that because of the way that the models used to define terms using Atlassian Intelligence in Confluence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that defining terms using Atlassian Intelligence in Confluence is less useful in scenarios where:
In addition, in Jira, we've also found that because defining terms using Atlassian Intelligence relies on search in Confluence, the feature will only work in Jira if you have permission to view a Confluence instance on the same site as your Jira instance. It's also possible that you might find that defining terms using Atlassian Intelligence doesn't perform as expected in Confluence spaces or Jira instances that have content written in multiple languages. |
| We understand you may have questions about how defining terms using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, defining terms using Atlassian Intelligence applies the following measures:
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| Atlassian Intelligence in editing experiences is powered by large language models developed by OpenAI and Google. These large language models include OpenAI's GPT series of models and Google's Gemini series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. |
| Atlassian Intelligence helps drive effective communication across all teams in an organization to improve efficiency, decision-making, and processes. We believe that using Atlassian Intelligence in editing experiences works best in scenarios like:
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| It’s important to remember that because of the way that the models used to power Atlassian Intelligence in editing experiences work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content they’re based on or include content that sounds reasonable but is false or incomplete. We’ve found that using Atlassian Intelligence in editing experiences is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how Atlassian Intelligence in editing experiences uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, Atlassian Intelligence in editing experiences applies the following measures:
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| Work item reformatter is powered by large language models developed by OpenAI, including OpenAI’s GPT series of models. Atlassian Intelligence uses this model to analyze and generate natural language within Jira. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models. |
| Work item reformatter helps improve the clarity of your Jira work item descriptions by reformatting them using a template developed by Atlassian. This template covers the types of information that we usually expect to see in a Jira work item description, such as a user story, context for the work, and acceptance criteria. We believe work item reformatter works best in scenarios where your work item descriptions already contain useful information (such as acceptance criteria or links to sources) but that information is not formatted using a clear or consistent structure. |
| It’s important to remember that, because of the way they work, the models that power work item reformatter can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, your reformatted description might not accurately reflect the content that it was based on, or it might include details that sound reasonable but are false or incomplete. We’ve found work item reformatter is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence, and always review the quality of the responses you get before sharing them with others. You might also want to think about reviewing and confirming that your work item descriptions include all relevant information before you start using work item reformatter to reformat them. |
| We understand you may have questions about how work item reformatter uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, work item reformatter applies the following measures:
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Summarize work item details in Jira Service Management
| Summarize work item details using Atlassian Intelligence is powered by large language models developed by OpenAI and Google. These large language models include OpenAI's GPT series of models and Google's Gemini series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Google’s models. |
| Instead of reading through long descriptions and numerous comments on a Jira Service Management work item, you can use Atlassian Intelligence to quickly summarize this information for you. This helps agents quickly understand the context of the work item and any progress made, enabling them to take swift action and provide timely assistance. We believe that summarizing work item details using Atlassian Intelligence works best for:
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| It’s important to remember that because of the way that the models used to power summarizing work item details using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that summarizing work item details using Atlassian Intelligence is less useful in scenarios when:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
| We understand you may have questions about how summarizing work item details using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, summarizing work item details using Atlassian Intelligence applies the following measures:
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| Summarize Smart Links with Atlassian Intelligence (AI) is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| After you hover over a Smart Link from Jira, Confluence, and Google Docs, Atlassian Intelligence can help you summarize the content, which allows you to determine the importance and value of the link and decide your next action. This reduces the need to leave the current page and switch contexts. We believe that Summarize Smart Links with AI works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power Summarize Smart Links with AI work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the summaries you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Summarize Smart Links with AI is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
| We understand you may have questions about how summarizing work item details using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, Summarize Smart Links with AI applies the following measures.
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| Jira Service Management’s virtual service agent is powered by large language models developed by OpenAI and Google, as well as open-source large language models (including the Llama series) . The virtual service agent uses these models as follows:
How large language models work: Large language models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they’ve been trained on. The large language models used to power the virtual service agent include OpenAI’s GPT series of models and Google’s Gemini series of models. Read more about the capabilities of OpenAI’s models and Google’s models. For more information on open-source language models, see information on the Llama series. |
| The virtual service agent helps teams automate tier-1 support interactions, powered by a conversational Atlassian Intelligence engine that analyzes and understands intent, context, and permissions to personalize interactions. Using Atlassian Intelligence, the virtual service agent helps teams scale their service desks and delight their customers with three key capabilities:
The virtual service agent is available in multiple channels, including Slack, Microsoft Teams, the Jira Service Management portal, and more. Read more about which channels are available for the virtual service agent. We believe that the virtual service agent works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power the virtual service agent, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, responses might not accurately reflect the content they’re based on, or they might include content that sounds reasonable, but is false or incomplete. We’ve found that the virtual service agent is less useful in scenarios where:
We encourage you to think about the situations when you’d use Atlassian Intelligence, and review the performance of the virtual service agent before you turn it on for customers. Read more about improving your virtual service agent’s performance. You might also want to think about:
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| We understand that you might have questions about how Jira Service Management’s virtual service agent uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, the virtual service agent applies the following measures:
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Summarize work item details using Atlassian Intelligence
| AI summaries in Jira is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
| Instead of reading through long descriptions and numerous comments on a Jira work item, you can use Atlassian Intelligence to quickly summarize this information for you. This helps agents quickly understand the context of the work item and any progress made, enabling them to take swift action and provide timely assistance. We believe that summarizing work item details using Atlassian Intelligence works best for work items with a large number of comments and/or lengthy comments and descriptions. |
| It’s important to remember that, because of the way they work, the models that power AI summaries in Jira can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. We’ve found that summarizing work item details using Atlassian Intelligence is less useful in scenarios when:
For this reason, we encourage you to consider the situations in which you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
| We understand you may have questions about how summarizing work item details using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, summarizing work item details using Atlassian Intelligence applies the following measures:
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AI Work Breakdown
| AI Work Breakdown is powered by large language models developed by OpenAI. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
| AI Work Breakdown suggests child work items based on a Jira work item you’ve created, making it easy to break down large pieces of work into smaller ones. Your work item is used as context to generate suggestions for child work item summaries and descriptions. We believe that AI Work Breakdown works best in scenarios where:
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| It’s important to remember that, because of the way that the models used to power AI Work Breakdown work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that AI Work Breakdown is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how AI Work Breakdown uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, AI Work Breakdown applies the following measures.
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Use AI to drive action
Select an Atlassian Intelligence feature below to get a transparent look at use cases and data use.
| Create incident with AI using Atlassian Intelligence is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your input and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they've been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| When escalating one or more alerts or alert groups to an incident in Jira Service Management, create incident with AI uses Atlassian Intelligence to quickly pre-populate all contextual information from for you to review as part of the incident creation process. This allows users to quickly understand the context of the incident created from those alerts or alert groups, and review and confirm pre-populated information including the title, description and priority of the alert when escalating it to an incident. We believe that create incident with AI works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power create incident with AI work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that create incident with AI is less useful in scenarios when:
For these reasons, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. To get the most useful results we suggest being as specific as possible in what you ask Atlassian Intelligence to do. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do. |
| We understand you may have questions about how create incident with AI uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, create incident with AI applies the following measures:
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Create post-incident review
| PIR (Post-Incident Review) creation by Atlassian Intelligence is powered by large language models developed by OpenAI. These large language models include OpenAI’s GPT series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on users' inputs and are probabilistic in nature. This means that the responses are generated by predicting the most probable next word or text, based on the data that they’ve been trained on. Read more about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
| PIRs are a core part of the incident management process, helping incident responders and managers learn from current incidents and pass along insights to prevent similar incidents in the future. Atlassian Intelligence helps to accelerate the often time-consuming task of compiling a PIR by suggesting a PIR description based on relevant contextual information in your Jira Service Management instance and chat tools like Slack for you to review. We believe that PIR creation using AI works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power PIR creation work, they can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that might sound reasonable but is false or incomplete. We’ve found that PIR creation using AI is less useful in scenarios where:
For this reason, we encourage you to think about situations where you can use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how create post-incident review using AI uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, PIR creation using AI applies the following measures.
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| Generating pull request descriptions with Atlassian Intelligence is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language and code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| Atlassian Intelligence can help you generate, transform, and summarize content while you're writing pull request descriptions or comments in the Bitbucket Cloud code review experience. This includes:
We believe that generating Bitbucket Cloud pull request descriptions with Atlassian Intelligence works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power this feature work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on or include content that sounds reasonable but is false or incomplete. We’ve found that generating Bitbucket Cloud pull request descriptions with Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how defining terms using Atlassian Intelligence in Confluence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, generating pull request descriptions with Atlassian Intelligence applies the following measures:
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Generate SQL queries in Atlassian Analytics
| Generating SQL queries using Atlassian Intelligence in Atlassian Analytics is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and understand natural language, then translates it to structured query language (SQL) within Atlassian Analytics. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| Ask Atlassian Intelligence a question in natural language and have it translated into SQL, rather than writing your own SQL queries from scratch. After you ask a question, Atlassian Intelligence uses the Atlassian Data Lake schema of your selected data source to generate an SQL query that can be used to build charts on your Atlassian Analytics dashboards, and can also help you learn about the schema in the Data Lake. We believe that generating SQL queries using Atlassian Intelligence works best in scenarios where:
Not sure what questions to ask?Here are some suggestions:
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| It’s important to remember that because of the way that the models used to generate SQL queries using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that generating SQL queries using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how generating SQL queries using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, generating SQL queries using Atlassian Intelligence applies the following measures.
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| Search answers in Confluence using Atlassian Intelligence is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| Knowledge bases are growing too fast for users to keep up. Searching answers in Confluence using Atlassian Intelligence provides a faster path to key information that customers need to move their work forward. This feature helps you easily find the information you need. It understands the types of questions you would ask a teammate, and answers them instantly. Find out more about using Atlassian Intelligence to search for answers in Confluence. We believe that searching answers in Confluence using Atlassian Intelligence works best when your Confluence site is full of detailed, complete, and up-to-date content. This feature does not generate new content, but searches Confluence pages and blogs (while respecting restrictions) to find an answer to your question. Atlassian Intelligence generates answers solely based on what’s in your Confluence, and what you, specifically, have access to. Not sure what questions to ask?Here are some suggestions
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| We understand you may have questions about how searching answers in Confluence using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, searching answers in Confluence using Atlassian Intelligence applies the following measures:
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Search work items in Jira
| Search work items using Atlassian Intelligence in Jira is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and understand natural language, then translates it to Jira Query Language (JQL) code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means their responses are generated by predicting the most probable next word or text based on the data they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| You can now ask Atlassian Intelligence what you want in everyday language instead of coming up with complex queries. By searching work items using Atlassian Intelligence, your prompt is translated into a JQL query which quickly assists you in your search for specific work items. We believe searching work items using Atlassian Intelligence works best in scenarios where:
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| It's important to remember that because of the way that the models used to search work items using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses you receive might not accurately reflect the content they are based on or include content that sounds reasonable but is false or incomplete. We've found that searching work items using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do. Make sure to include the exact fields and values you're looking for. |
| We understand you may have questions about how searching work items using Atlassian Intelligence uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, searching work items using Atlassian Intelligence applies the following measures:
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AI Work Breakdown
| AI Work Breakdown is powered by large language models developed by OpenAI. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read about the capabilities of OpenAI’s models, or about this approach in OpenAI’s research papers. |
| AI Work Breakdown suggests child work items based on a Jira work item you’ve created, making it easy to break down large pieces of work into smaller ones. Your work item is used as context to generate suggestions for child work item summaries and descriptions. We believe that AI Work Breakdown works best in scenarios where:
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| It’s important to remember that, because of the way that the models used to power AI Work Breakdown work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that AI Work Breakdown is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how AI Work Breakdown uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, AI Work Breakdown applies the following measures.
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| Suggested topics in knowledge base is powered by large language models developed by OpenAI and Anthropic, as well as a combination of open-source transformer-based language models and other machine learning models. These large language models include OpenAI’s GPT series of models and Anthropic’s Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. The open-source encoder models convert your textual inputs into numerical forms (embeddings) which are used for identifying and forming topics from your inputs. These large language models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models and Anthropic’s models. For more information on open-source language models, see information on Multi-QA-miniLM and E5-Multilingual. |
| This feature helps admins and agents understand the gaps in their knowledge base by analyzing the service requests received in a project. This feature clearly highlights the topics for which help seekers are raising requests (based on data in the last 30 days) but there’s no existing knowledge. By suggesting topics, we want to give project admins and agents visibility into how many requests can be deflected or at least resolved with knowledge. We believe that increasing the number of knowledge articles will influence the performance of other features in Jira Service Management such as Virtual Service Agent’s AI answers. When admins or agents create articles on the suggested topics, it can also help improve the resolution rate of requests resolved using AI answers. We believe that suggested topics work best in scenarios where:
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| It’s important to remember that because of the way that the models used to power suggested topics in knowledge base work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that suggested topics in knowledge base is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how suggested topics uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, suggested topics in knowledge base applies the following measures. Your suggested topics in knowledge base:
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Search content in Confluence
| Searching Confluence content using Atlassian Intelligence is powered by large language models developed by OpenAI and Google. These large language models include OpenAI’s GPT series of models and Google’s Gemini series of models. Atlassian Intelligence uses these models to analyze and generate natural language, then translates it to CQL (Confluence Query Language) code within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models. |
| You can now ask Atlassian Intelligence for the content you want to find in Confluence using everyday language instead of coming up with complex queries. With the help of Atlassian Intelligence, your prompt is translated into a CQL query which quickly assists you in your search for specific content. We believe that Searching Confluence content using Atlassian Intelligence works best in scenarios where:
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| It’s important to remember that because of the way that the models used to power Searching Confluence content using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Searching Confluence content using Atlassian Intelligence is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about being as specific as possible in what you ask Atlassian Intelligence to do. Make sure to include the exact fields and values that you’re looking for. |
| We understand you may have questions about how Searching Confluence content using Atlassian Intelligence uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, Searching Confluence content using Atlassian Intelligence applies the following measures.
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Read more about Atlassian Intelligence
Glean instant insights from your data
Select an Atlassian Intelligence feature below to get a transparent look at use cases and data use.
| Chart insights is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series, Phi series, and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series and the Phi series. |
| Chart insights uses Atlassian Intelligence to help speed up your understanding of data in any chart in Atlassian Analytics. It does so by using the dashboard title, chart title, and chart data (including column headers and row values) to generate a natural language summary of that chart and its data. It will also aim to identify any trends or anomalies to provide you with certain insights into that chart. We believe that Chart insights work best in scenarios where:
Bar charts, line charts, and bar-line charts work best with this feature since they typically have trends, dates, and many rows of data. |
| It’s important to remember that because of the way that the models used to power Chart insights work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Chart insights is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how Chart insights uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, Chart insights applies the following measures.
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| Suggest request types using Atlassian Intelligence is powered by large language models developed by OpenAI. These large language models include OpenAI's GPT series of models. Atlassian Intelligence uses these models to analyze natural language input and generate recommendations for request type names and descriptions for you within Jira Service Management. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. Read more about the capabilities of OpenAI’s models. |
| Spend less time figuring out what kind of request types you need to create for your project, and instead get suggestions from Atlassian Intelligence. Simply describe your work and what your team typically manages, to see what types of requests you could create. Select one of the suggestions generated by Atlassian Intelligence to create a request type. Find out more about how to use Atlassian Intelligence to suggest request types. We believe that using Atlassian Intelligence to suggest request types works best in scenarios where:
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| It’s important to remember that because of the way that the models used to suggest request types using Atlassian Intelligence work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that using Atlassian Intelligence to suggest request types is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. You might also want to think about:
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| We understand you may have questions about how using Atlassian Intelligence to suggest request types uses your data. This section supplements the information available on our FAQ page. We process:
When it comes to your data, using Atlassian Intelligence to suggest request types applies the following measures.
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| Focus Area Executive Summary is powered by large language models developed by OpenAI. These models include the OpenAI models described here. Atlassian Intelligence uses these models to analyze and generate natural language within our products. These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on. |
| Focus Area Executive Summary uses Atlassian Intelligence to provide a quick, actionable summary of your focus area, including the work that’s in progress, the health of connected goals, suggestions for where to pay attention, and recommendations to remediate any work items. The Focus Area Executive Summary works best when:
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| It’s important to remember that because of the way that the models used to power Focus Area Executive Summary work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable. For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete. We’ve found that Focus Area Executive Summary is less useful in scenarios where:
For this reason, we encourage you to think about the situations when you use Atlassian Intelligence and review the quality of the responses you receive before sharing them with others. |
| We understand you may have questions about how Focus Area Executive Summary uses your data. This section supplements the information available on our Trust Center. We process:
When it comes to your data, Focus Area Executive Summary applies the following measures.
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