Why AI gives vague debugging answers — and how to fix it with better prompts

Here's a scenario most Python developers have hit.

A Celery task works perfectly in local development. Works in staging. Fails in production - 5% of requests, no obvious pattern.

It fails intermittently — maybe 5% of requests — with no obvious pattern.

The error:

DetachedInstanceError: Instance <Payment> is not bound to a Session

You paste it into an AI assistant.

You get:
"

Check your database connection string. Try restarting the workers. Maybe add some logging.
"

That's not really debugging.

That's guessing.

The problem isn't necessarily the AI.

It's the prompt.

A generic question usually produces a generic answer.

Give the AI the debugging context first

Instead of asking:

Why am I getting this error?

give the model the information a senior engineer would actually want:

Act as a senior Python debugging engineer.
Prioritise evidence over assumptions.
Concurrency context: CELERY
Failure pattern: INTERMITTENT (~5% of requests)
Last known good: deploy v2.3.1, 3 days ago
Error (full, unedited):
DetachedInstanceError: Instance is not
bound to a Session; attribute refresh operation
cannot proceed
Relevant code:
[paste task code + session setup here]
Analyse:

1. Identify the most likely root cause and the evidence supporting it.
2. Give up to 3 alternative explanations.
3. Pay particular attention to session lifetime, object state, and task boundaries.
4. Propose the smallest safe code change.
5. Explain what conditions could make the failure intermittent.
6. Give me a regression test or load-test scenario that could confirm the hypothesis.

Notice what changed.

You're no longer asking AI to guess the fix.

You're giving it a structured investigation.

The response becomes much more useful

The AI identifies the root cause immediately: a session-bound ORM object is crossing a task boundary and being accessed after its originating SQLAlchemy session has closed — which is why the failure is intermittent. Under normal load, the task starts before the session closes. Under connection pool pressure, it doesn't.

The safer architecture is usually to pass a stable identifier to the worker and load the object using a session owned by that worker:

Avoid passing session-bound ORM state

process_webhook.delay(payment.id)
@celery.task
def process_webhook(payment_id: int) -> None:
with SessionLocal() as session:
payment = session.get(Payment, payment_id)
# process payment
...

Now the worker controls the lifecycle of the database session.

And instead of simply saying "try this fix," the AI can help you test the hypothesis:

• Does the failure correlate with worker concurrency?
• Does it occur when the originating request/session has already ended?
• Is lazy loading or attribute refresh happening after detachment?
• Can the failure be reproduced under load?
• Does reloading the object inside the worker eliminate the failure?

That's the difference between AI-generated guesses and an actual debugging workflow.

The bigger lesson:

The quality of an AI coding answer often depends on the quality of the problem structure you give it.

Most debugging sessions fail not because the AI is incapable — but because the prompt gives it nothing to work with.

For difficult bugs, include things like:

Context → Failure pattern → Evidence → Relevant code → Constraints → Hypotheses → Verification

You're essentially giving the AI a debugging framework instead of asking it to magically know what's wrong.

I turned this approach into a reusable prompt

The Debugging Detective is one of 10 structured prompts in my Python AI Prompt Pack.

The pack covers:

• Debugging & root-cause analysis
• Python boilerplate generation
• Refactoring & cleanup
• Automation
• Performance optimization
• pytest test generation
• Coding roadblocks
• Async & distributed systems
• Type-safety adoption
• Pandas & NumPy optimization

Each prompt gives you the structure, instructions, and fields needed to get more useful reasoning from an AI coding assistant.

4 of the 10 prompts also include sample AI outputs, so you can see the kind of response the prompt is designed to produce.

Works with ChatGPT, Claude, Gemini, or any LLM.

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If you found this useful, the full Debugging Detective prompt — along with 9 others
covering performance, async systems, type safety, and more — is in the
Python AI Prompt Pack.
$9 · Works with ChatGPT, Claude, and any LLM.