A Better Bug-Finding Prompt

A small prompt change can shift how an agent reads code. One report says bug detection rose from 39% to 89% with a specific prompt. That is a strong result, but it is still one report.

The useful part is the pattern. If an agent is told to inspect code with a narrow success criterion, it can miss whole classes of defects. If you ask it to review like a skeptic, it can surface more issues before a human sees the diff.

What the signal suggests

This points to a common failure mode in agentic coding: the model optimizes for finishing the task, not for stress-testing the result. In practice, that can produce code that looks fine, passes a quick read, and still misses edge cases, regressions, or broken assumptions.

A bug-finding prompt works when it changes the job from “make this work” to “try to break this and explain where it fails.” That matters most in tasks with hidden state, branching logic, integrations, or tests that are easy to satisfy superficially.

The source does not give the exact prompt, so there is no single magic wording here. The broader lesson matters more than the phrasing.

A prompt pattern worth trying

Use a review-first prompt that forces the agent to inspect the code from multiple angles before it edits anything.

A practical version looks like this:

• State the intended behavior in plain language.
• List the most likely failure modes.
• Check the implementation against each one.
• Look for missing tests, weak assertions, and false positives.
• Report only concrete issues, with file and line references when possible.
• If the code is fine, say why and note what remains unverified.

This works better than asking for “bugs” in the abstract. The agent needs a frame. Otherwise it may drift into vague criticism or style comments instead of real defects.

Where it helps most

This pattern is most useful when the change is small enough to inspect end to end, but complex enough that a quick pass is unreliable. Good candidates include:

• validation logic
• state transitions
• API handlers
• test suites
• refactors that touch multiple files
• edge-case-heavy business rules

It is less useful for mechanical work like renaming symbols or moving files. In those cases, the extra review may not pay off.

How to use it in a team workflow

A simple workflow is to separate generation from review.

First, let the agent implement the change. Then run a second pass with a review prompt that assumes the code may be wrong. That second pass should not rewrite the task from scratch. Its job is to inspect, challenge, and surface defects.

In team settings, this can sit between build and human review:

1. Agent makes the change.
2. Tests run.
3. A review pass looks for missed bugs.
4. A human checks the highest-risk findings.

That sequence creates a cheap filter before someone spends time reading the diff.

Tradeoffs and limits

The main tradeoff is false confidence. A prompt that finds more issues can also sound more certain than it should. If the agent is not grounded in tests, traces, or concrete code paths, it may invent problems or overstate risk.

There is also a cost in time. A second review pass adds latency and token use. For small tasks, that may be wasteful. For larger changes, the extra pass can be cheaper than a missed bug.

Another limit is that prompt quality is only one part of the system. If the agent cannot run tests, inspect logs, or read the relevant files, the review will be shallow no matter how good the wording is.

The 39% to 89% result also needs replication. It is interesting, but not enough to generalize across repos, languages, or task types. Treat it as a local result that needs your own measurement.

A practical way to test it

If you want to know whether this helps in your environment, measure it on a small sample of real tasks.

Pick 10 to 20 recent changes and compare two review modes:

• baseline review: the agent checks the diff normally
• bug-hunting review: the agent must enumerate failure modes before commenting

Track how many real issues each mode finds, how many are false positives, and how often the review changes the final code. If the second pass consistently catches missed edge cases without flooding you with noise, it is probably worth keeping.

That kind of test is simple enough to run in a week and grounded enough to trust.

A methodology note

This is a good place to Test before standardizing the prompt. A small comparison on real diffs is more useful than adopting a new review pattern because it sounds sharper. See our methodology.

Bottom line

The signal is not that one prompt fixes bug finding. It is that agent behavior changes when you ask for adversarial review instead of task completion.

If your current workflow mostly asks agents to produce code, add a second pass that asks them to attack it. Keep the prompt concrete, require evidence, and measure the result on your own codebase. That is more reliable than chasing a universal prompt that claims to catch everything.