AI Prompt Templates for Code Review
20+ templates for code review. Automated code review for security vulnerabilities, performance issues, best practices, and maintainability with the STCO framework.
Why Code Review Needs Structured Prompts
Automated code review for security vulnerabilities, performance issues, best practices, and maintainability. Without structure, AI outputs for code review are generic and require heavy editing. The STCO framework (Situation, Task, Constraints, Output) ensures every prompt produces usable, specific results by encoding your exact requirements upfront. This eliminates the trial-and-error cycle that wastes time and API credits.
STCO Framework for Code Review
Situation: Define the context — who is the audience, what is the current state, what background does the AI need? Task: Specify exactly what deliverable you need — be precise about the scope. Constraints: Specify the language, framework, and your team's coding standards as constraints. Output: Define the format, length, and structure of the response you need. This four-part structure produces dramatically better results than freeform prompting.
Common Mistakes to Avoid
The most common mistake when using AI for code review: Pasting code without context about its purpose, the codebase architecture, or what "good" looks like for your team. Other pitfalls include not iterating on your prompts (treating the first output as final), ignoring the model's strengths and limitations, and failing to provide examples of what "good" output looks like. STCO addresses all of these by forcing you to think through requirements before prompting.
Template Library: 20+ templates
AI Prompt Architect provides 20+ templates specifically designed for code review. Each template follows the STCO framework and has been tested across GPT-4o, Claude 4, and Gemini 2.5 for consistent quality. Templates include real-world examples, suggested model settings (temperature, max tokens), and guidance on when to use each variant.
Getting Started
Start with our most popular code review template, customise the Situation and Constraints sections for your specific context, and generate your first output. The STCO Prompt Scorer will evaluate your prompt's structure and suggest improvements. Most users see a 40-60% quality improvement in their AI outputs within their first session.
FAQs
What are the best AI prompts for code review?
The best prompts for code review use the STCO framework: define the Situation (context and audience), Task (specific deliverable), Constraints (specify the language, framework, and your team's coding standards as constraints), and Output (format and length). This structure produces specific, actionable results instead of generic AI output.
Can AI really help with code review?
Yes — AI excels at automated code review for security vulnerabilities, performance issues, best practices, and maintainability when given structured prompts. The key is providing enough context and constraints. Pasting code without context about its purpose, the codebase architecture, or what "good" looks like for your team — STCO-structured prompts solve this by encoding all requirements upfront.
Which AI model is best for code review?
For code review, we recommend starting with GPT-4o or Claude 4 for their strong general capabilities. Gemini 2.5 excels when you need to process large documents. The STCO framework works across all models, so you can switch freely based on your needs and budget.
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