In a recent workshop, I demonstrated a code review workflow using Claude Sonnet. The entire thing took less than two minutes. No extended thinking. No complex chains. Just a well-structured prompt and a focused review. The audience reaction was immediate: "That's it?"

That is exactly the point. Most developers overcomplicate AI-assisted code reviews. They write wall-of-text prompts, expect the AI to understand their entire codebase context, and then complain when the output misses the mark.

The secret is structure. Not verbosity. This guide gives you copy-paste prompt templates for code reviews, PR summaries, and architecture decisions. These work with Claude, ChatGPT, or any capable LLM.

The Anatomy of a Good Prompt

Before diving into templates, understand the four components of an effective prompt:

  1. Role: Tell the AI what persona to adopt.
  2. Input: Provide the code or diff in a clean format.
  3. Output: Specify exactly what you want back.
  4. Constraints: Set boundaries to prevent hallucinations or overreach.

Missing any of these leads to mediocre results. A prompt without constraints might suggest rewriting your entire architecture when you only wanted a logic check. A prompt without a clear output format might give you a vague summary instead of actionable feedback.

Template 1: The Quick Code Review

Use this for everyday PRs and diffs. It keeps effort low and focus high. Perfect for Claude Sonnet in standard mode.

Prompt:

### Role
Act as a senior software engineer conducting a code review.

### Input
Here is the diff/code:

[PASTE DIFF OR CODE SNIPPET]

### Output
Review this for:
1. Logic errors or bugs
2. Security issues (SQL injection, XSS, hardcoded secrets)
3. Performance concerns
4. Code readability issues

Format your response as bullet points. Max 3 sentences per point.

### Constraints
- Do not suggest architectural changes unless there is a critical flaw.
- Do not rewrite the entire codebase.
- Focus only on what changed in the diff.
- If the code looks good, say "LGTM" and list one minor nitpick at most.

This template works because it sets expectations. The AI knows to be concise, to avoid overreach, and to focus on the specific change rather than hypothetical improvements.

Before vs. After

Before (Unstructured): "Review this code."

Result: A vague, five-paragraph response that touches on everything from variable naming to microservice architecture, with no clear action items.

After (Structured): Use the template above.

Result: Three bullet points. One identifies a missing null check. One flags an unused variable. One suggests a more efficient loop. You can immediately address each or dismiss them.

Template 2: The PR Summary Generator

Writing PR descriptions is tedious. Let AI do the first pass, then you edit. This template connects directly to GitHub or GitLab via Claude's connectors if you have them enabled, or you can paste the diff manually.

Prompt:

### Role
Act as a technical lead writing a pull request summary for stakeholders.

### Input
Here is the git diff:

[PASTE git diff OUTPUT OR USE CONNECTOR]

### Output
Write a PR description with these sections:
1. **Summary:** 2-3 sentences explaining what changed and why.
2. **Technical Details:** Bullet points of key implementation notes.
3. **Testing:** How this was tested (or ask me to add this section).
4. **Breaking Changes:** List any breaking changes or write "None."

Keep the entire description under 200 words.

### Constraints
- Use clear, non-technical language in the Summary section.
- Do not invent features or tests that don't exist.
- If the diff is incomplete, tell me what context you need.

The constraint about not inventing features is critical. LLMs love to hallucinate tests or configuration changes that never happened. This constraint forces the AI to stick to what actually changed.

Template 3: The Architecture Decision Prompt

For bigger decisions, you want higher effort and deeper reasoning. This is when you might use extended thinking or simply give the AI more context. Treat this as a consultant, not a rubber stamp.

Prompt:

### Role
Act as a principal software architect with 15 years of experience.

### Input
I am considering [TECHNOLOGY/ARCHITECTURE] for a new project.

Context:
- Project scale: [SMALL/MEDIUM/LARGE]
- Team size: [N] developers
- Expected traffic: [DESCRIPTION]
- Time constraints: [DEADLINE OR ONGOING]

### Output
Analyze this choice based on:
1. Suitability for the use case
2. Team learning curve and adoption
3. Long-term maintainability
4. Cost implications (infrastructure, licenses, or time)

Provide a recommendation with a confidence score (Low/Medium/High) and list the top 3 risks.

### Constraints
- Do not blindly recommend the latest trend.
- Consider practical factors like hiring and onboarding.
- If you don't have enough context, ask specific questions.

This prompt shifts from "check for bugs" to "evaluate a decision." The confidence score and risk list make the output immediately actionable for stakeholders who need to sign off on technical choices.

When to Use Extended Thinking

During the workshop demo, I used standard Sonnet mode for the code review. No extended thinking. Why? Because the task was straightforward and the code fit in context.

Use extended thinking (or models like Opus) when:

Skip extended thinking for:

The rule of thumb: higher effort for decisions, lower effort for reviews. You want speed in your daily workflow, depth in your monthly planning.

Integrating with GitHub, GitLab, and Jira

If you use Claude Code or the Claude desktop app, you can enable connectors for GitHub, GitLab, and Jira. This lets the AI read your PRs, issues, and tickets without copy-pasting.

Why this matters:

When the AI can see the full PR context—including linked issues, previous comments, and commit history—its reviews improve dramatically. It can reference the original requirement, check if the implementation actually addresses it, and flag scope creep.

Practical tip:

When setting up connectors, give the AI read-only access initially. Let it summarize PRs and flag issues. If it proves reliable, you can expand its role. But never let it auto-merge or push changes without human approval.

Common Mistakes to Avoid

Even with templates, developers make mistakes that reduce output quality.

Pasting Too Much Code

If your diff is 2000 lines, the AI will lose context or truncate its response. Break it into logical chunks or focus on the critical files. Ask the AI to review only the core logic, not the boilerplate.

Missing Context

Don't paste a function in isolation and ask if it's "good." The AI doesn't know your constraints. Provide context about why this code exists, what problem it solves, and any relevant architectural guidelines.

Accepting Output Blindly

AI can hallucinate. It might suggest a library that doesn't exist or a pattern that conflicts with your team's standards. Treat AI output as a suggestion, not a directive. Verify before applying.

Building Your Template Library

The templates in this guide are starting points. As you use them, you will discover patterns specific to your workflow. Save those variations.

Create a "Prompts" file in your notes app (Obsidian, Notion, or even a plain text file) with these sections:

The time you save on repetitive tasks compounds. A two-minute code review becomes thirty seconds. A ten-minute PR description becomes two minutes of editing AI output.

Put It Into Practice

This week, try the following:

  1. Copy the Quick Code Review template and use it on your next PR. Compare the output to your usual review process.
  2. Generate a PR summary using the template. Edit it down to your team's usual style. Notice how much faster the initial draft is.
  3. Save your variations. If you modify a template for your specific stack, save that variation.

AI-assisted code reviews are not about replacing human judgment. They are about scaling your attention. A well-structured prompt catches the obvious issues so you can focus on the subtle ones. That is the practical value.

Frequently Asked Questions

Can I use these prompts with ChatGPT or other models?

Yes. These templates are model-agnostic. Claude, ChatGPT, and other capable LLMs all respond well to structured prompts with role, input, output, and constraints. You might need to adjust formatting slightly depending on the model.

Do I need extended thinking for good code reviews?

No. Most code reviews benefit from standard models. Extended thinking is overkill for diffs under a few hundred lines. Save extended thinking for architecture decisions or complex refactors where deep reasoning matters more than speed.

Should I let AI auto-comment on my PRs?

Not yet. Let it generate suggestions, but have a human post them. AI can miss nuance, flag false positives, or misinterpret the context. Use AI as a pre-review tool, not a replacement for human review.

What if the AI misses a bug?

Then you found a case where the prompt needs refinement. Add more context about that specific class of bugs to your template. AI improves with iteration—both in how you prompt and in how it learns from your feedback.