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What a backlog-ready ticket needs to get right
A polished description is not necessarily an accurate or actionable one. Compare each draft with its source—such as a user report, prompt, screenshot, or support note—and check the ticket’s content and metadata separately. The following questions provide a practical review framework, not a universal scoring standard.
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- Fidelity: Does the draft preserve the request without adding an unsupported cause, implementation choice, impact, or reproduction step?
- Context: Does it follow the repository’s issue form, template, and terminology?
- Actionability: Is the desired outcome clear, and are the details needed to act on it available?
- Evidence: Are reported behaviors, screenshots, and reproduction steps supported by the source?
- Uniqueness: Is the item the same work as an existing issue, or merely related?
- Metadata and risk: Are labels, priority, issue type, assignee, and any proposed close action warranted?
GitHub’s guidance on reviewing AI-generated code recommends checking context, intent, and possible misunderstandings. Those are useful review principles for tickets too, but code-review examples do not establish a ticket error rate. GitHub also advises users to review and refine Copilot’s issue drafts before creating them. GitHub’s review guidance and issue-drafting instructions support that distinction.
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Review the draft in six passes
1. Keep the original request beside it
Read the source and the draft together. Verify that the ticket describes what the reporter actually asked for, rather than a plausible interpretation of what might be wrong. If the source does not identify a cause, proposed solution, user impact, or reproduction detail, remove that assertion or frame it as an open question. Do not let an AI fill a gap with certainty the evidence does not provide.
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2. Check every field against the team’s template
Inspect the title, problem or task description, expected result, reproduction steps where relevant, acceptance criteria, labels, issue type, and assignee. Use the repository’s issue form or template as the structure, and confirm that each populated field means what the team expects it to mean. GitHub Copilot can draft issue titles, bodies, labels, assignees, and other metadata, and can map a prompt to an issue form or template. Populated fields are still suggestions, not validation. GitHub labels the feature public preview and says it may change; availability can depend on plan and configuration.
3. Decide whether it is actionable
Ask whether a teammate can understand the requested outcome, what evidence is available, and what information remains missing. If a blocker prevents useful work, request the specific missing detail or mark the ticket as needing information under your team’s process. Avoid treating fluent prose as a substitute for a clear requirement. GitHub’s AI issue-intake guidance describes suggestions to request more information or mark an issue actionable, and directs maintainers to review those suggestions and decide what to do.
4. Check for duplicate and related work
Search the backlog for the same failure, requested change, or outcome. A similar phrase or symptom is only a lead: determine whether the existing issue represents the same work or a distinct, related problem. Link related issues where that helps the team; do not close or merge a ticket based on textual similarity alone. GitHub’s example AI triage workflow distinguishes duplicates from related issues and recommends adapting labels and priority definitions to local conventions: AI issue triage on GitHub.
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Review each suggested label, priority, type, assignee, project field, and status change on its own merits. A reasonable description does not make its metadata correct. If an automation can alter fields or close issues, inspect its rationale and confidence where those are available. Hold ambiguous or low-confidence changes for human approval, then accept or decline suggestions after checking them. GitHub documents automations that can change issue attributes and controls for exposing rationale, confidence, and approvals; available controls depend on repository configuration and product availability. See GitHub’s documentation on rationale, confidence, and approvals.
6. Record a clear decision
Create or accept the issue only after resolving material errors. If it is blocked, ask a focused question or return the draft for revision. The decision should leave the team with a ticket that accurately states the requested work and fits its process—not merely one that reads well.
When the draft is not ready
- Unsupported detail: Remove it, qualify it as unconfirmed, or ask the reporter to verify it.
- Unclear outcome: Request the expected result before treating the work as actionable.
- Missing evidence: Ask for the relevant reproduction steps, environment, or screenshot rather than presenting absent evidence as fact.
- Possible duplicate: Compare the underlying work with the existing issue; preserve a link if the items are related but distinct.
- Questionable metadata or closure: Hold the change for review and inspect the rationale and confidence if the automation provides them.
AI output can sound plausible while misunderstanding context or constraints. GitHub’s responsible-use guidance discusses these risks for agents and code; apply that warning as a reason to check ticket claims, not as evidence that tickets fail at a particular rate. GitHub’s responsible-use guidance does not establish a benchmark for AI-generated ticket accuracy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—show
GitHub’s documentation supports a human review workflow for its own issue-drafting, intake, and automation features. It does not establish a universal ticket-quality score, a measured rate of AI ticket errors, or equivalent feature availability across issue trackers. Use the checklist as a practical process and adapt field names, approval steps, and readiness rules to your team’s repository and tools.
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