An AI-Assisted Code Review Workflow That Scales Downward
Pair LLM summaries with human judgement — fast first-pass triage, risk tagging, and where automation should stop.
Table of Contents
An AI-Assisted Code Review Workflow That Scales Downward
Large teams built sophisticated review cultures; tiny squads often merge quickly with minimal commentary. Large language models can bridge the gap by highlighting probable defects early — if you constrain scope and never confuse summarisation for authority.
Stage 1 — automated lint & tests
Run formatters, static analysis, and CI suites before asking humans or models — noise drowns useful signal.
Stage 2 — LLM diff digest
Ask for: risky zones, missing tests, inconsistent naming, or migration hazards — not final approval. Keep prompts anchored to file paths and hunks actually changed.
Stage 3 — human reviewers focus on judgement
Architecture trade-offs, product nuances, and malicious patterns remain human strengths. Publish review guidelines so teammates interpret AI notes consistently.
Guardrails
Strip secrets before sending snippets externally; prefer vendor APIs with explicit data policies when handling proprietary code.
Treat AI output as conversation starters — cite checks humans performed instead of rubber-stamping machine summaries.
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