Debugging Assistant Prompt

Q: Should I trust the root cause explanation without verifying it myself?

No -- treat it as a strong hypothesis to test, not a confirmed diagnosis. AI debugging has real limits, particularly around distributed systems, timing-dependent bugs, and issues that depend on production-scale data the model never sees. Verify the proposed root cause against your own evidence before trusting the fix built on top of it.

Q: Is this template different from the code review prompt template?

Yes -- they solve different problems. The code review template evaluates working code against a scope like security or readability. This one diagnoses code that's actively broken, using an error and stack trace as the starting evidence rather than a general review pass.

“Fix this bug” without the full error, the stack trace, and what you’ve already tried produces a guess dressed up as an answer. A model asked to debug with real evidence in front of it — and asked to explain the root cause before proposing a fix — produces something closer to an actual diagnosis. Teams using AI-assisted debugging with this kind of structured input have reported meaningfully faster resolution times; the gap between a vague prompt and a well-evidenced one is usually the difference between a guess and a real fix.

This template asks for root cause explanation before any fix, and for the minimal fix rather than a broader rewrite — models that explain why something failed tend to produce fewer regression patches than models that just patch the visible symptom.

The core template

Act as a senior [language/framework] engineer debugging this issue.

Language/framework/runtime and relevant versions: [e.g., Python 3.12, Django 5.0, PostgreSQL 16]

Error message: [paste the exact, full error message]
Full stack trace: [paste the complete trace -- not just the top line]

Relevant code: [paste the function/file where the error originates, plus anything it directly calls]

What I've already tried: [list any fixes or debugging steps already attempted, and what happened with each]

Timeline / recent changes: [when did this start? Any recent deploy, dependency update, or config change?]

First explain the root cause -- not just what's failing, but why. Then propose the minimal fix needed, not a broader rewrite. If you need more information to be confident in the diagnosis, ask for it rather than guessing.

Why each slot matters

Full error message and complete stack trace. Not just the top line — the full trace shows the actual call path that led to the failure, which is often where the real cause sits, several frames away from where the error surfaced.
Versions. Many bugs are version-specific — a behavior that’s correct in one framework or library version and broken in another. Without this, the model may diagnose against the wrong version’s behavior entirely.
Only the relevant code. The function where the error originates, plus what it directly calls — not the whole file or repository, which dilutes focus without adding useful signal.
What you’ve already tried. Skipping this is the most common way to waste a debugging round — without it, the model may confidently suggest something you already ruled out.
Timeline and recent changes. A bug that appeared right after a dependency update or config change points toward a very different root cause than one that’s always been there — this context often narrows the diagnosis significantly on its own.
“Root cause first, then minimal fix.” This ordering matters — a model that explains why something broke before proposing a change is less likely to patch the visible symptom while leaving the actual cause in place to resurface later.

Three filled-in examples

Example 1 — A backend exception

“Act as a senior Python engineer debugging this issue. Language/framework: Python 3.12, FastAPI 0.115, SQLAlchemy 2.0. Error message: ‘sqlalchemy.exc.DetachedInstanceError: Parent instance is not bound to a Session.’ Full stack trace: [pasted]. Relevant code: [the endpoint handler and the model class it queries]. What I’ve already tried: added session.refresh() before the return, which didn’t help. Timeline: started after upgrading SQLAlchemy from 1.4 to 2.0 last week. First explain the root cause, then propose the minimal fix.”

Example 2 — A frontend rendering bug

“Act as a senior React engineer debugging this issue. Language/framework: TypeScript, React 19, Next.js 15. Error message: ‘Hydration failed because the server rendered HTML didn’t match the client.’ Full stack trace: [pasted]. Relevant code: [the component in question]. What I’ve already tried: wrapping the dynamic content in useEffect, which fixed the warning but broke initial render. Timeline: appeared after adding a date-formatting library. First explain the root cause, then propose the minimal fix.”

Example 3 — An intermittent production failure

“Act as a senior backend engineer debugging this issue. Language/framework: Node.js 22, Express, Redis for caching. Error message: intermittent ‘ECONNRESET’ errors, roughly 2% of requests. Full stack trace: [pasted]. Relevant code: [the Redis client initialization and the middleware using it]. What I’ve already tried: increased the connection pool size, no change in error rate. Timeline: started roughly two weeks ago, no corresponding deploy on our side that we’ve identified. First explain the root cause, then propose the minimal fix — if the cause might be outside our own code, say so explicitly rather than only suggesting internal changes.”

Common mistakes this template helps avoid

Pasting only the top line of a stack trace. The actual root cause is often several frames deeper than where the error message itself appears — truncating the trace can hide exactly the information needed.
Omitting what’s already been tried. This is the single most common way a debugging round gets wasted — the model confidently re-suggests something already ruled out, and a full cycle is spent finding that out again.
Asking for a fix before a diagnosis. Skipping straight to “how do I fix this” tends to produce a fix for the symptom rather than the cause, which often means the same bug resurfaces in a different form later.
Leaving out version numbers. A surprising number of bugs are specific to one version of a framework or library — without this, the model may reason from a version’s behavior that doesn’t match what you’re actually running.

Frequently asked questions

What if I don’t have a full stack trace, just an error message?

Include whatever you have and say explicitly that the trace is incomplete — a partial trace with that context is still more useful than none, and it tells the model not to assume it has the full call path. For genuinely trace-less errors (some frontend or intermittent issues), lean more heavily on the timeline and “what you’ve tried” sections to compensate.

How many debugging rounds should I expect before it’s resolved?

Well-evidenced prompts often resolve in one or two rounds for straightforward bugs. If you’re past three rounds without a working fix, that’s usually a sign the underlying diagnosis is wrong, not that you need a fourth attempt at the same framing — worth stepping back and gathering more direct evidence (added logging, a reproduction script) before continuing.

Should I trust the root cause explanation without verifying it myself?

No — treat it as a strong hypothesis to test, not a confirmed diagnosis. AI debugging has real limits, particularly around distributed systems, timing-dependent bugs, and issues that depend on production-scale data the model never sees. Verify the proposed root cause against your own evidence before trusting the fix built on top of it.

Does this work for debugging AI agent behavior, not just traditional code errors?

The same root-cause-first principle applies, but agent debugging often needs different evidence — the full execution trace of what the agent tried, saw, and decided at each step, not just a single error message. For an agent that’s stuck or looping, understanding why a specific step failed matters more than the final error state alone.

Is this template different from the code review prompt template?

Yes — they solve different problems. The code review template evaluates working code against a scope like security or readability. This one diagnoses code that’s actively broken, using an error and stack trace as the starting evidence rather than a general review pass.

Next steps

Once a fix is identified and verified, consider running the Code Review Prompt Template against the change itself before merging, particularly if the fix touches security- or correctness-sensitive code. For more ready-to-use templates, browse the Prompt library.