Why use a prompt builder instead of just asking for a review?

Generic requests get generic answers. A structured prompt forces the model to focus on the areas you care about, rank findings by severity, and return a concrete fix for each issue rather than vague "looks good" feedback.

What's the difference between the depth levels?

Quick pass flags only clear bugs and security holes. Standard covers correctness, edge cases, security, and maintainability. Deep does a line-by-line review including concurrency, performance, API design, and testability.

Does it run the review for me?

No. It builds the prompt locally in your browser — no API key needed. You paste your code into the prompt and run it in ChatGPT, Claude, or any model you prefer.

Which output format should I choose?

Grouped by severity is best for reading; the Markdown table is easy to scan; inline comments map findings to lines; JSON is ideal if you want to feed results into another tool or CI step.

Can I review more than one language?

Yes. Pick the language each time you build a prompt. The instructions adapt to the language so the model uses idiomatic conventions and the correct comment syntax for inline output.

Code Review Prompt Builder

Code review prompt builder

LLMs are surprisingly good code reviewers — but only if you ask precisely. A bare “review this code” tends to produce praise and a few surface nits. The code review prompt builder assembles a structured prompt that fixes the language, sets the review depth, narrows to the areas you actually care about, and pins down the output format, so the model returns ranked, actionable findings with concrete fixes.

How it works

You choose a language, a review depth (quick pass, standard, or deep), the priority areas to focus on — security, performance, readability, correctness, tests, API design — and an output format. The tool composes a prompt that instructs the model to base every comment on the code it can actually see, to explain each issue and give a specific fix, and to format results the way you asked (grouped by severity, a table, inline comments, or JSON). Everything is generated locally; there’s no API key and no network call. You paste your code into the marked block and run it in any LLM.

Tips and notes

Match depth to the moment. A quick pass is right for a hotfix PR; reserve deep reviews for security-sensitive or concurrent code.
Fewer focus areas, sharper review. Checking everything dilutes attention; pick the two or three areas that matter most for this change.
Use JSON output for automation. It drops cleanly into a CI annotation step or a dashboard, where free-form prose can’t.
Keep the code self-contained. Paste enough context that the model isn’t guessing — the prompt explicitly forbids inventing code it can’t see.