Writing a bug report prompt that actually gets a useful response is harder than it looks. These ready-to-use Prompt Templates for Bug Reports fix the vague, one-line reports that waste your API credits and your time.
I've tested these across Claude, GPT-4, Gemini, and DeepSeek. They all work, though Claude tends to give the most thorough root-cause analysis while GPT-4 is better at spotting edge cases in the reproduction steps.
Why Generic Prompts Fail Here
"Here's my error, what's wrong?" gets you a surface-level answer. The LLM doesn't know your stack, your expected behavior, or what you've already tried. So it guesses — and you get a generic response about checking your null values.
The failure mode is context starvation. A good bug report prompt forces the LLM to ask for or receive the five things it needs: expected behavior, actual behavior, reproduction steps, environment, and error logs. Without all five, you're gambling on the model's ability to read your mind.
Template 1: The Structured Repro
This one works for 80% of runtime errors and logic bugs. It forces you to provide the exact information a senior dev would demand.
You are a senior debugging engineer. I'm reporting a bug in my codebase.
**Expected behavior:**
[What should happen]
**Actual behavior:**
[What actually happens]
**Reproduction steps:**
1. [Step one]
2. [Step two]
3. [Step three]
**Relevant code snippet(s):**
[Paste the minimal code that reproduces the issue]
**Environment:**
- Language/Runtime: [e.g., Node 20, Python 3.12]
- Framework: [e.g., Express, Django]
- Key dependencies: [e.g., Prisma 5.0, axios 1.6]
**Error message/logs (if any):**
[Paste the full stack trace]
**Task:**
Identify the most likely root cause. Then provide a fix. If you need clarification, ask exactly one question — otherwise, do not ask questions.
The placeholders are self-explanatory. Fill in every single one. If you skip the environment section, you'll get a fix that assumes the wrong package version or runtime behavior.
Template 2: The Hypothesis-Driven Debug
Use this when you have a suspicion but need validation. It's built for intermittent bugs or issues that only happen in production.
You are a debugging specialist. I have a bug that I cannot reliably reproduce.
**Symptom:**
[What the user reports or what the logs show]
**My leading hypothesis:**
[What I think is causing it]
**Evidence supporting my hypothesis:**
[Log snippets, timing data, specific user actions]
**Evidence against my hypothesis:**
[Anything that contradicts it]
**Code paths involved:**
[Which functions/modules are in the call chain]
**What I've already tried:**
[Do not repeat these suggestions]
**Task:**
Assess my hypothesis. Tell me if it's plausible, and if not, give me 2-3 alternative hypotheses ranked by likelihood. For each alternative, specify what evidence would confirm or refute it. Keep your response under 300 words.
The key here is the "evidence against" section. Most developers only share supporting evidence, which biases the LLM toward confirming your theory. Forcing the contradiction improves the analysis significantly.
Template 3: The Post-Mortem Edge Case
This one's for the nasty ones: race conditions, memory leaks, or bugs that only appear after 48 hours of uptime. It's overkill for simple issues, but it's the only thing that works for production-only failures.
You are a production incident investigator. I have a bug that only manifests under specific load or timing conditions.
**System architecture:**
[Brief description: e.g., Next.js frontend, Node API, Postgres, Redis cache]
**Failure signature:**
[What breaks, what the metrics show, error rates]
**Conditions present when it fails:**
[e.g., >500 concurrent users, memory usage >80%, specific request pattern]
**Conditions when it does NOT fail:**
[e.g., works fine under 100 users, works with cold cache]
**Relevant configuration:**
[Timeouts, connection pool sizes, retry logic]
**Logs from the failure window:**
[Paste 10-20 lines around the incident]
**Task:**
Identify the most probable root cause from this evidence. Focus on race conditions, resource exhaustion, or timing-dependent failures. Give me a detection strategy (what to log/metric to add) and a mitigation. Do not suggest restarting the server or adding more memory unless there's evidence that's the fix.
This template works because it forces you to describe the negative space — when it doesn't fail. That's usually where the root cause hides.
How to Adapt These for Your Own Codebase
Three concrete adjustments make these templates dramatically better.
First, paste your actual error format into the template. If you use Sentry, include the Sentry event ID and the breadcrumbs format. The LLM will start matching patterns from your specific stack.
Second, add your team's naming conventions to the environment section. If your codebase uses userService instead of UserService, say so. The LLM will reference your actual symbols in its answer, which makes the fix directly copy-pasteable.
Third, keep a local file with your top 10 dependencies and their versions. Paste that in instead of writing them out each time. It saves two minutes per report and prevents version mismatch hallucinations.
Do These Prompts Work With Any LLM?
Yes, but with caveats. All four major models — Claude, GPT-4, Gemini, DeepSeek — handle Template 1 and Template 2 well. Template 3 is where they diverge. Claude gives the best architectural reasoning. GPT-4 is strongest at pattern-matching the failure signature to known issues. Gemini is surprisingly good at suggesting metrics to add for detection. DeepSeek is the weakest on Template 3 — it tends to suggest generic fixes like "optimize your database queries" without evidence.
For daily use, stick with Template 1. It's the most reliable across models and produces actionable fixes roughly 90% of the time when you fill it out completely.
The one adjustment that improves these prompts the most: always include the "do not suggest" line in each template. Telling the LLM what to avoid — whether it's restarting the server or repeating your tried fixes — cuts useless suggestions by half and forces it to think harder.