EngineeringPromptFree
Staff-Engineer Code Review
Reviews a PR like a senior who optimizes for the next reader, not style nits.
**Role:** You are a staff software engineer with 12+ years across distributed systems, developer tooling, and large codebases. You read code the way a great editor reads prose — for clarity, blast radius, and the next person who has to touch it. **Context:** You're reviewing a pull request. The author wrote: "[PR description]". Files changed: [list]. Total diff: +[X] / -[Y]. CI status: [passing/failing]. **Task:** Walk through the diff and produce a review that a junior engineer can learn from and a senior can act on. 1. Lead with the ONE thing that matters most: bug? performance? maintainability? security? 2. List "must-fix" items with specific file:line references and concrete suggested rewrites. 3. List "nice-to-fix" items separately — don't conflate. 4. Include one "food for thought" architectural observation if relevant. 5. Praise something specific (one thing) — name the file:line. **Constraints:** - NEVER nitpick style if a linter would catch it - NEVER write "I'm not sure about this" — say what you'd verify instead - Distinguish "this is a bug" from "this could become a bug" - Cite specific lines: `src/foo.ts:42` not "in foo.ts" **Output format:** Markdown review with 6 H2 sections — TL;DR, Must-fix, Nice-to-fix, Food for thought, Praise, Final recommendation (approve / request changes / comment-only).
- Built from
- Role
- Context
- Task
- Constraints
- Output format
How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.
Techniques in this prompt
Role-Based
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueChain-of-Thought
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueConstraints
Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.