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claudiacodacy merged 3 commits into from
Mar 3, 2026
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docs: jira ticket metadata and reviewer context CF-2209 #2604

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docs: jira ticket metadata and reviewer context CF-2209
manufacturist Mar 3, 2026
e7c0e4f
docs: #2604 feedback CF-2209
manufacturist Mar 3, 2026
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Merge branch 'master' into feat/cf-2209-jira
claudiacodacy Mar 3, 2026
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12 changes: 9 additions & 3 deletions docs/codacy-ai/codacy-ai.md
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## What is Codacy AI?

Codacy AI is a set of optional features integrated into the Software designed to optimise development workflows and elevate code quality standards through automated issue descriptions, actionable recommendations and false-positive detection, is available for the Customer.
Codacy AI is a set of optional features integrated into the Software, designed to optimise development workflows and elevate code quality standards through automated issue descriptions, actionable recommendations and false-positive detection. These features are available for the Customer.

Codacy AI utilizes only enterprise-grade instances of OpenAI and Google Gemini services with enhanced security, privacy, and data protection features. Customer Code processed through Codacy AI will not be used by Codacy, OpenAI, or any third-party AI provider for training, improving, or developing artificial intelligence models, machine learning algorithms, or any other automated systems.

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!!! note
AI Reviewer is currently only available on GitHub, for all Team and Business plans.

_This feature leverages Google Gemini models, and is strictly opt-in: it will only run on repositories or projects where a repository admin has enabled it._
_This feature leverages Google Gemini models, and is strictly opt-in; it will only run on repositories or organizations where an admin has enabled it._

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[vale] reported by reviewdog 🐶
[Microsoft.Adverbs] Consider removing 'strictly'.

Raw Output:
{"message": "[Microsoft.Adverbs] Consider removing 'strictly'.", "location": {"path": "docs/codacy-ai/codacy-ai.md", "range": {"start": {"line": 69, "column": 54}}}, "severity": "WARNING"}

The AI Reviewer combines the reliability of deterministic, rule-based static code analysis with the enhanced context and prioritization capabilities of AI. It draws in the necessary context from source code and PR metadata to ensure the business intent matches the technical outcome, and can catch logic gaps that conventional scanners (and human reviewers) often miss.
The AI Reviewer combines the reliability of deterministic, rule-based static code analysis with the enhanced context and prioritization capabilities of AI. It draws in the necessary context from PR metadata, Jira ticket if [integration exists](../organizations/integrations/jira-integration.md), source code, and Codacy data to ensure the business intent matches the technical outcome, and can catch logic gaps that conventional scanners (and human reviewers) often miss.

More details about [AI Reviewer here](../repositories-configure/integrations/github-integration.md#ai-reviewer).

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4. Save your changes. Once enabled, Codacy will start adding a Summary to your pull requests based on the AI-enriched reviews.
5. To request a PR review, click **Run Reviewer** in the Summary or call our [public API](https://api.codacy.com/api/api-docs#triggerpullrequestaireview). Your review will be published as soon as it's ready.

#### Jira context for the AI Reviewer

If the Jira integration is enabled, we will automatically attempt to detect the Jira ticket key (ABC-123, case-insensitive) in the Pull request metadata. If found, the ticket title and description will be used to enrich the context of our reviewer.

More about the [Jira integration here](../organizations/integrations/jira-integration.md).

#### Custom Instructions

To improve the results of the AI Reviewer, you can provide custom instructions to the AI Reviewer. These instructions are specific to a repository and help the AI Reviewer understand the structure of the code, the business logic of the project, and your own preferences.
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9 changes: 9 additions & 0 deletions docs/organizations/integrations/jira-integration.md
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!!! important
Uninstalling the Jira integration as described above deletes all associated open items. You can alternatively uninstall the Jira integration on the Jira website: this doesn't delete anything, but it prevents Codacy from opening new Jira-related items.

## AI reviewer context

!!! tip
AI Reviewer is currently only available on GitHub, for all Team and Business plans.

If the repository or organization has the AI Reviewer enabled, it will automatically check the Pull Request metadata for a Jira ticket key (ABC-123, case-insensitive). The title and description will be fetched to enrich the review context, and the process will cross-check the required deliverable and acceptance criteria with the existing code changes.

More details about [AI Reviewer here](../../repositories-configure/integrations/github-integration.md#ai-reviewer).

## Creating Jira tickets from issues, dependencies and files

Installing the Jira integration enables the feature of creating Jira tickets from issues, dependencies and files
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2 changes: 1 addition & 1 deletion docs/repositories-configure/integrations/github-integration.md
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Expand Up @@ -48,7 +48,7 @@ When enabled, Codacy posts a **Codacy's Analysis Summary** comment on your pull
!!! note
If you enable the AI Reviewer, consider disabling AI-enhanced comments to avoid receiving duplicate comments on Codacy issues.

The AI Reviewer extends Pull request review with an AI-powered code analysis. It combines the reliability of deterministic, rule-based static code analysis with the enhanced context and prioritization capabilities of AI. It draws in the necessary context from source code and PR metadata to ensure the business intent matches the technical outcome, and can catch logic gaps that conventional scanners (and human reviewers) often miss.
The AI Reviewer extends Pull request reviews with an AI-powered code analysis. It combines the reliability of deterministic, rule-based static code analysis with the enhanced context and prioritization capabilities of AI. It draws in the necessary context from PR metadata, Jira ticket if [integration exists](../../organizations/integrations/jira-integration.md), source code, and Codacy data to ensure the business intent matches the technical outcome, and can catch logic gaps that conventional scanners (and human reviewers) often miss.

It provides feedback on missing or weak tests, complex or duplicated code, and keeps security concerns up to date. Beyond that, it adds contextual insights about whether the changes follow the requirements, business rules, and logic used in the project.

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