Prompt Debugging Lab: Troubleshooting Common Failures and Escalation Prompts for Non-Engineers

AI-powered workflows often break at the prompt level—sometimes subtly, sometimes catastrophically. While engineers have well-developed debugging methods, non-technical teams need their own toolkit for diagnosing and repairing failures. This lab provides actionable, field-tested approaches to troubleshooting prompt-based issues.

Common Prompt Failure Modes

1. Hallucination (Incorrect Facts): AI outputs confident-sounding but incorrect information.2. Ignoring Constraints: Model misses required instructions (e.g., form/length/format).3. Inconsistent Persona/Tone: Outputs vary in brand voice, tone, or persona within or across sessions.4. Verbosity/Format Errors: Responses break structural requirements, e.g., giving lists when asked for bullet points.

20 Essential Repair Prompts

Anti-Hallucination:

1. "For each factual claim, cite your source or state if you are unsure."
2. "Indicate your confidence (High/Medium/Low) for each major statement."
3. "Before answering, repeat back your knowledge cutoff."

Length and Format:

4. "Your response must be exactly [N] words. Reject any overage."
5. "Summarize in three stages: full, 50% reduction, one line."
6. "Output valid JSON using this schema: {\"result\":string, \"confidence\":string, \"sources\":array}."

Constraint Enforcement:

7. "List the three main constraints I gave you before you answer."
8. "Do not use [banned element]. See this bad example: [example]."
9. "After you answer, explain which requirements you met and how."

Persona and Tone:

10. "You are [Persona]. Remind yourself at start: 'I am [Persona]'."
11. "Maintain [Tone]. If I say 'tone check', adjust at once."
12. "Use brand voice: [description], avoid these: [list]."

Advanced Diagnostics:

13. "Reason step by step about your instructions and solution."
14. "Address at least one likely edge-case scenario."
15. "Summarize prior conversation context in 2 sentences, then answer."

Quality Assurance:

16. "Critique your own answer: strengths, weaknesses, improvement."
17. "Give two options; justify best fit for requirements."
18. "State all assumptions you made to answer."

Emergency Escalation:

19. "Prompt isn't working. 1) Describe what I want. 2) Why might response fail? 3) Suggest 2 alternatives."
20. "Ignore all prior instructions. Begin fresh: [clear restatement]."

Guided Diagnostic Testing

• ISAP Method:
• Isolate—test prompt with varied inputs
• Structure—break up, simplify prompt steps
• Adjust—change settings or instruction order
• Promote—if repeatable, prepare escalation
• Data vs. Prompt Quick Checks:
• Data issue? = Fails only on specific topics, references old data, not format
• Prompt issue? = Fails across topics, right info/wrong format, instructions ignored

Escalation Templates

Standard:Incident ID, who/impact, reproduction steps (prompt, input, expected, actual), troubleshooting attempted, tech details (model/version/context), suggested next steps for engineering.

Critical:Impact summary, timeline, copy of prompt, 3+ failure cases, business requirements/deadlines, contacts.

Post-Mortem:Summary, root cause analysis, timeline, lessons, action items, process improvements.

Advanced: RAG & Hallucination Mitigation

• Retrieval-Augmented Generation (RAG) reduces hallucination by grounding AI in external data.
• Techniques: Real-time fact-checking, contextual retrieval, enforced citation.

Team Best Practices

• Version control for prompts
• Schedule prompt audits
• Cross-functional debugging sessions
• Document failures and learning

Success Metrics

• Accuracy rate
• Consistency across runs
• User satisfaction
• Escalation rate
• Mean time to resolution

Ready for prompt debugging at scale? JMK Ventures offers workshops and custom automation for AI-embracing teams.