AI Generation Context Section
The first section of the AI-optimized PR template: generation context. This section tells the reviewer HOW the code was generated, which saves review time by setting expectations. Fields: AI tool used (Claude Code, Cursor, Copilot), generation method (agentic multi-step, inline edit, chat-generated), and human modifications (list of changes made after AI generation). The context: helps the reviewer understand the code's origin. Code that was AI-generated and then manually refined: requires different review attention than code that was fully AI-generated with no modifications.
Why generation context matters: AI-generated code has predictable strengths and weaknesses. Strengths: consistent convention compliance (when rules are good), comprehensive happy-path handling, clean structure. Weaknesses: edge case coverage, business logic correctness, performance optimization. When the reviewer knows the code is AI-generated: they can skip convention checking (the rules handled it) and focus on edge cases and business logic (where the AI is weakest). When the reviewer does not know: they review everything with equal attention, wasting time on convention checking that was already handled.
The generation context also documents the prompt. Include a brief summary of the prompt used: 'Prompt: Create user notification preferences API with CRUD operations, zod validation, and integration tests.' The prompt summary: helps the reviewer understand the developer's intent. If the generated code does not match the intent: the prompt needs refinement, not the code. The reviewer can suggest: 'The code does X, but based on the prompt I think you wanted Y — should we re-generate with a clearer prompt?' AI rule: 'Generation context transforms PR review from guessing game to informed process. The reviewer knows: which tool generated the code, what the developer intended, and what was manually modified. This context: reduces review time by 20-30% because the reviewer focuses on the right things.'
Rule Compliance Attestation
The second section: a checklist of rule compliance items. The developer attests: 'I verified that the AI-generated code follows our CLAUDE.md conventions.' Specific checkboxes: error handling uses project pattern (Result/try-catch/error codes), imports follow project conventions (named/default, ordering), tests follow project framework and structure (Vitest/Jest, describe/it), file structure follows project organization (correct directory, correct naming). The attestation: shifts convention verification from reviewer to developer.
The attestation is not just a formality. When the developer checks each box: they actually review the AI-generated code against the rules. This self-review: catches 15-20% of convention violations before the reviewer sees them. The checkboxes: serve as a reminder to verify each convention category. Without the checklist: developers submit AI-generated code without reviewing it against rules. With the checklist: the self-review is built into the PR submission process.
Edge case attestation: beyond convention compliance, include a checkbox for edge case review. 'I verified: empty state handling, null/undefined handling, boundary values, error responses.' The developer: confirms they checked the areas where AI-generated code most commonly fails. The reviewer: trusts that basic edge cases are covered and focuses on subtle business logic issues. AI rule: 'Rule compliance attestation is the developer's gate check before the reviewer's review. Two checkpoints: developer self-review (attestation) and reviewer deep review (PR review). The double-check: catches more issues than either alone.'
Developer submits PR without self-review. Reviewer finds: 3 convention violations, 2 missing edge cases, 1 wrong import pattern. Round-trip time: 4 hours. Developer submits PR with the attestation checklist. Self-review catches: 2 convention violations, 1 import pattern. Remaining for reviewer: 1 convention edge case, 2 business logic questions. Round-trip time: 2 hours. The checklist: not extra work for the developer. It is work that WOULD happen during review, moved earlier where it is cheaper to fix. Two hours saved per PR across 10 PRs per sprint: 20 hours of team time recovered.
Review Focus Areas Section
The third section: review focus areas. The developer tells the reviewer WHERE to focus attention. Format: 'I am confident about: [areas where the AI-generated code is well-tested and rule-compliant]. I am less confident about: [areas that need careful review — complex business logic, novel patterns, security-sensitive code].' The focus areas: direct the reviewer's attention to the highest-value items.
Examples of effective focus areas: 'Confident: CRUD operations, input validation, test coverage. Less confident: the notification deduplication logic — the AI's approach uses a Set-based dedup that may not handle race conditions. Please review the dedup logic in notification-service.ts lines 45-67.' The specific file and line reference: tells the reviewer exactly where to look. The explanation of concern: tells them what to look for. The reviewer: starts with the most impactful review item instead of reading the entire diff linearly.
The focus area section also prevents over-review. Without it: the reviewer spends equal time on every file. The CRUD controller: 10 minutes of review for code that the rules already validated. The dedup logic: 5 minutes of review for the one area that actually needs human judgment. With focus areas: the reviewer spends 2 minutes confirming the CRUD is standard and 15 minutes deeply reviewing the dedup logic. The total review time: similar. The review quality: significantly higher on the areas that matter. AI rule: 'Review focus areas are the PR equivalent of a surgeon's pre-op briefing. The team knows: what is routine (CRUD), what is complex (dedup), and where to concentrate attention. The briefing: makes the review both faster and more thorough.'
A PR with 15 files changed. Without focus areas: the reviewer reads all 15 files with equal attention. Time: 2 hours. 12 files are standard CRUD (convention-compliant, rule-validated). 3 files contain novel business logic. With focus areas: 'Confident: 12 CRUD files. Less confident: dedup logic in notification-service.ts:45-67, rate limiter in middleware.ts:20-35, webhook retry in events.ts:80-100.' The reviewer: skims 12 files (20 minutes), deeply reviews 3 focus areas (40 minutes). Total: 1 hour. Same quality, half the time.
Test Coverage and Verification Section
The fourth section: test coverage summary. Fields: test count (unit: X, integration: Y, e2e: Z), coverage percentage (lines, branches), AI-generated tests vs manually written tests, and test execution results (all passing, any skipped). The summary: gives the reviewer confidence that the code is tested before they start reading it. A PR with 0 tests: requires the reviewer to mentally simulate every code path. A PR with 15 passing tests: the reviewer trusts the happy path and focuses on untested scenarios.
AI-generated test quality indicator: note which tests were AI-generated. AI-generated tests: tend to test implementation details (function was called) rather than behavior (output is correct). The reviewer: pays extra attention to AI-generated tests, checking that they test the right things. A PR that says '12 tests, 8 AI-generated, 4 manually written for edge cases': tells the reviewer that the developer identified the AI's testing gaps and filled them. This transparency: builds reviewer trust.
Verification steps: include a section for manual verification steps the reviewer can follow. 'To verify: 1) Run pnpm test src/notifications. 2) Start dev server, navigate to /settings/notifications. 3) Toggle a preference and verify it persists on reload.' The verification steps: enable the reviewer to quickly confirm the feature works end-to-end. For AI-generated code: verification is especially important because the code may be syntactically correct but functionally wrong. AI rule: 'The test coverage section answers the reviewer's first question: is this code tested? The answer: determines how deeply the reviewer needs to inspect. High coverage with quality tests: efficient review. Low coverage: the reviewer must compensate by reading every line carefully.'
Scenario A: developer submits PR. Reviewer does not know if it is AI-generated. Finds a suspicious pattern. Thinks: 'Did the developer understand this, or did they blindly accept AI output?' Trust: uncertain. Scenario B: developer submits PR with generation context. 'Generated with Claude Code. Prompt: notification CRUD with validation. Human modifications: added rate limiting logic manually, refined the dedup approach.' The reviewer: knows exactly what was AI-generated and what was human-modified. Trust: clear. After 10 PRs with transparent context: the reviewer trusts the developer's AI coding process. The template: builds that trust systematically.
PR Template Quick Reference
The AI-optimized PR template structure.
- Section 1 — Generation Context: AI tool used, generation method, prompt summary, human modifications
- Section 2 — Rule Compliance: error handling, imports, tests, file structure checkboxes + edge case attestation
- Section 3 — Review Focus: confident areas (routine), less confident areas (needs attention), specific file/line references
- Section 4 — Test Coverage: test count by type, coverage percentage, AI vs manual tests, verification steps
- Key benefit: reviewer knows where to focus before reading a single line of code
- Self-review: the attestation checklist catches 15-20% of issues before reviewer sees them
- Time savings: 20-30% faster reviews because focus is directed to high-value areas
- Trust building: transparency about AI generation builds reviewer confidence in the process