Code Review Checklist and Actionable Feedback AI Prompt
Code reviews often stall with vague comments and missed issues. You might get “clean this up” without examples or “optimize” with no benchmarks. That slows teams, hides bugs, and leaves standards unclear. A strong prompt turns scattered opinions into precise, prioritized, and testable feedback.
This example shows how to ask for role-aware, standards-based review with concrete suggestions and risk-based prioritization. AskSmarter.ai guides you through the right questions—language, context, performance targets, security policies, and constraints—so your prompt captures exactly what matters.
Use this to get structured findings, reproducible steps, and ready-to-apply diffs. You’ll speed up reviews, improve consistency, and ship higher-quality code with fewer back-and-forths.
The transformation
Before — Vague prompt
Review my code and tell me what to fix or improve.
After — Optimized prompt
You are a senior backend engineer. Review the following Python 3.11 PR touching a Flask API and Postgres. Audience: mid-level developer. Goals: improve readability, performance, and security.
Provide:
- Summary of top 5 issues by risk.
- Specific diffs or code snippets for fixes.
- Complexity notes (Big-O) and DB query count impact.
- Tests to add with example test names.
- References to PEP8 and OWASP ASVS 4.0 items.
Constraints: keep suggestions within current architecture; target p95 latency ≤ 200ms. Code follows:
<paste PR diff here>Why this works
The after prompt wins because it adds clarity, context, and structure that drive precise output.
- Clarity: It names the role (senior backend engineer) and the tech stack (Python 3.11, Flask, Postgres), so feedback aligns with domain expertise.
- Context: It states goals (readability, performance, security) and constraints (keep architecture, p95 ≤ 200ms). This narrows solutions to what you can apply now.
- Structure: It requests a five-part format: risk-ranked summary, diffs, complexity notes, tests, and standards references. That ensures actionable, auditable feedback.
- Tone and audience: It defines the reader (mid-level developer), so explanations are instructive without being overly basic.
AskSmarter.ai gets you here by asking targeted questions about language versions, frameworks, performance targets, security baselines, and team conventions. Those answers become instructions that eliminate guesswork, reduce rework, and produce checklists you can execute immediately.
When to use this prompt
Marketing Engineering Teams
Review web service changes for performance and security before launch. Ensure suggestions fit current roadmap and SLA targets.
Product Managers
Request structured code feedback tied to feature goals and latency budgets to unblock releases without deep technical debates.
Sales Engineers
Assess demo environment code for reliability and quick-win improvements before customer trials, with minimal architectural change.
Customer Success Engineers
Audit customer-specific integrations for security and maintainability using clear, standards-referenced recommendations.
Engineering Leaders
Standardize review quality across teams with consistent, risk-prioritized feedback and testable action items.
Pro tips
- 1
Set measurable targets like p95 latency or memory ceilings to focus recommendations.
- 2
Name your standards (PEP8, OWASP, internal style guide) so references map to your processes.
- 3
Constrain scope to current architecture to avoid churn from large refactors during a release.
- 4
Provide representative input sizes and sample payloads to ground performance and complexity analysis.
More coding & technical examples
Code Optimization Suggestions Report AI Prompt
Backend API Endpoint Design Specification AI Prompt
Database Migration Plan and Script Generator AI Prompt
Build a prompt for your situation
This example shows the pattern. AskSmarter.ai guides you to create prompts tailored to your specific context, audience, and goals.