That wastes hours. Real debugging requires hypothesis-driven investigation.
You get:
- vague, non-actionable suggestions
- the same “check your syntax” advice repeated
- rabbit holes that don’t address the root cause
- fixes that introduce new bugs
- hours lost on trial and error
But debugging is not magic.
It follows predictable logic:
- reproduce the context → isolate variables → test hypotheses
- contradiction between expected and actual behavior points to root cause
- confirmation test before fix prevents guesswork
- minimal changes reduce regression risk
Without structure, debugging becomes random.
This prompt forces AI to act like a senior debugger — structured, testable, precise.
Assume the role of a senior debugging engineer who finds root causes, not symptoms. Your task is to analyze an error and provide a structured diagnosis. Generate: 1. CONTRADICTION - One sentence restating the gap between expected and actual behavior 2. ROOT CAUSES (ranked 1-5) - List each with reasoning based on error signature - #1 most likely must have direct evidence from inputs 3. CONFIRMATION TEST - Minimal, isolated test (code or command) to confirm #1 root cause - Do not propose a full fix yet 4. MINIMAL FIX - Exact code change (diff format or before/after) - No extra refactoring 5. RISKS - Data loss, security, race conditions, performance INPUTS: LANGUAGE/Framework: [PASTE LANGUAGE AND FRAMEWORK] Full Error Message: [PASTE COMPLETE STACK TRACE OR ERROR TEXT] Relevant Code Snippet: [PASTE CODE WHERE ERROR OCCURS] Expected Behavior: [WHAT SHOULD HAPPEN] Actual Behavior: [WHAT ACTUALLY HAPPENS] What I've Tried: [LIST ATTEMPTED FIXES] RULES: - Never skip the confirmation test - Rank root causes by likelihood, not convenience - Flag dangerous side effects explicitly - If error message is unfamiliar, explain which keywords matter for searching - Suggest search queries when appropriate
- Copy the full stack trace — not just the last line. The prompt works best with exception type, line numbers, and context.
- Be honest about “what I’ve tried” — this prevents the AI from suggesting the same dead ends.
- Run the confirmation test first — don’t skip to the fix. That single step separates guesswork from diagnosis.
- Use for any language — Python, JavaScript, Go, Rust, PHP, even SQL errors. Fill the inputs precisely.
- Iterate — paste the confirmation test output back into a new run if the root cause changes.
LANGUAGE/Framework: Python / FastAPI
Full Error Message:
sqlalchemy.exc.IntegrityError: (psycopg2.errors.NotNullViolation) null value in column “user_id” violates not-null constraint
Relevant Code Snippet:
@router.post(“/review”)
def add_review(review: ReviewCreate, db: Session = Depends(get_db)):
new_review = ReviewModel(rating=review.rating, comment=review.comment)
db.add(new_review)
db.commit()
return {“status”: “ok”}
Expected Behavior:
Review saved with user_id automatically from logged-in user session.
Actual Behavior:
IntegrityError: user_id cannot be null.
What I’ve Tried:
Checked that user is authenticated (prints user email in logs). Added print(type(review.user_id)) → None. Tried setting default=0 in DB schema.
This framework improves outcomes by forcing:
- contradiction statement (clarity)
- ranked root causes (prioritization)
- confirmation test (verification before action)
- minimal fix (precision, not scope creep)
- risk flagging (safety in production)
Great debugging doesn’t guess — it forms hypotheses, tests them, and fixes exactly what’s broken.
Build Better AI Systems
Subscribe for advanced prompt engineering, AI coding tools, debugging frameworks, and practical strategies for developers and engineers.