AI System Prompt for a Code Review Bot That Only Flags Blocking Issues

0 views Published Nov 9, 2026

This system prompt configures an AI code review bot to comment only on issues serious enough to block a merge — security flaws, correctness bugs, broken tests — while staying silent on style nitpicks and personal preference. It's built for engineering teams running an AI reviewer on pull requests who are tired of the bot burying real problems under dozens of low-value formatting comments.

The prompt works by giving the model an explicit severity scale and a strict rule: if an issue doesn't meet the "blocking" bar, it either stays unsaid or gets grouped into a single optional note at the end, never mixed in with the real findings. This keeps the bot's output short enough that developers actually read it, instead of learning to skim past it.

Because the hardest part of a prompt like this is defining exactly where the "blocking vs. not blocking" line sits, it's worth stress-testing that definition before deploying it. Running the system prompt through Cuelara's Prompt Debugger can surface the edge cases it doesn't yet cover, like a performance issue that's only blocking in a hot path.

Prompt template

prompt-template

304 tokens

You are an AI code review bot. Your job is to flag only issues that should block a pull request from being merged. Context: - Language/stack: [e.g., TypeScript, Python, Go] - What already runs automatically: [e.g., ESLint, Prettier, unit tests, type checker] - Release standards: [e.g., no unhandled errors, no hardcoded secrets, no breaking API changes without a version bump] Severity definitions: - BLOCKING: security vulnerabilities, logic bugs that produce wrong output, broken or missing error handling for likely failure cases, breaking changes without migration notes, hardcoded secrets or credentials. - NOT BLOCKING: formatting, naming preferences, minor style choices, anything the linter or formatter already enforces. Task: Review the following diff and list only BLOCKING issues. For each one, include the file and line reference, a one-sentence explanation of why it blocks the merge, and a specific suggested fix. If there are zero blocking issues, say so explicitly and do not invent minor comments to fill space. Non-blocking observations, if any, go in a single short "Optional notes" section at the end, clearly separated from the blocking list. Diff to review: [PASTE DIFF OR PULL REQUEST CONTENT HERE] Output format: 1. "Blocking Issues" — numbered list, or "No blocking issues found." 2. "Optional Notes" — short bullet list, omit this section entirely if there's nothing worth mentioning.

Want it sharper? Optimize this prompt with Prompt Optimizer, check it with the Prompt Debugger or shorten it with the Token Optimizer.

Example input

example-input

91 tokens

Language/stack: Python What already runs automatically: Black formatter, flake8, pytest Release standards: no unhandled exceptions on user input, no hardcoded credentials Diff to review: def get_user_balance(user_id): api_key = "sk_live_49fA..." response = requests.get(f"https://api.bank.com/users/{user_id}/balance", headers={"Authorization": api_key}) return response.json()["balance"]

When to use it

You're setting up an AI reviewer on pull requests and want it to stop drowning real bugs in style comments.
Your team already has a linter and formatter, so you need the AI reviewer to focus only on what those tools can't catch.
You're onboarding a bot into a repo with strict release gates and need it to clearly separate blocking issues from suggestions.
You want a consistent review bar across PRs regardless of which engineer is reviewing or which model generates the comments.

Best practices

Give the model concrete examples of what counts as blocking versus non-blocking for your specific codebase, not just abstract categories.
Have the bot state its confidence on each flagged issue, since a confidently wrong blocking comment does more damage than a missed one.
Ask the bot to quote the exact line or diff hunk it's commenting on, so reviewers don't have to hunt for context.
Review and tune the severity boundary after the first week of real PRs — the initial line almost always needs adjusting.

Common mistakes

Leaving "blocking" undefined, which lets the model apply its own judgment and drift back toward commenting on style.
Not telling the bot what to do when it finds zero blocking issues, so it invents minor nitpicks just to have something to say.
Letting the bot comment on generated code, vendored dependencies, or test fixtures that shouldn't be reviewed the same way.
Skipping a severity scale entirely and asking the model to just "focus on important stuff," which it interprets inconsistently across runs.

FAQs

Why does my AI code review bot comment on style instead of real bugs?

Most models default to listing everything they notice, including style, unless the prompt explicitly ranks issues by severity and tells the model what to omit. Defining a clear "blocking vs. not blocking" line, like in this prompt, is what fixes that.

Should this replace human code review entirely?

No. It's meant to catch a specific class of issue before a human reviewer looks at the PR, not to replace judgment calls about architecture, trade-offs, or team conventions that a bot can't fully evaluate.

How do I stop the bot from flagging issues in generated or vendored code?

Tell it explicitly in the context section which paths or file patterns to skip (for example, build output, vendored dependencies, or auto-generated client code), since the model has no way to know your repo's conventions unless you state them.

How can I check this system prompt's quality before deploying it on real pull requests?

Prompt Debugger — it's built to catch vague constraints and missing edge cases in exactly this kind of rules-based system prompt, and Prompt Formatter can tighten the output structure so the bot's comments post consistently to your PR tool every time.

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