AI Recovery — Frequently Asked Questions
Common questions about recovering from bad AI outputs — when to fix, when to restart, and how to prevent failures.
Your Questions Answered ❓
When should I actually start a new conversation instead of recovering?
Start fresh when: (1) the conversation has become so long that the AI is losing context, (2) you have fundamentally changed what you need and the existing context is misleading, or (3) you have tried 3+ recovery attempts and the AI keeps making the same mistake. If the conversation is under 10 exchanges and the direction is broadly right, recovery is almost always faster.
Why does AI hallucinate, and can I prevent it?
AI hallucination happens because language models generate probable text, not verified facts. They are trained to produce coherent, confident-sounding responses — even when they do not know the answer. Prevention strategies: ask the AI to say "I don't know" when uncertain, request citations, avoid asking for very specific statistics (these are often fabricated), and always verify claims that feel too convenient.
The AI keeps making the same mistake even after I correct it. Why?
Usually because your correction is too vague or the original prompt is continuously misleading the AI. Try: (1) quote exactly what was wrong and state exactly what the correct version should be, (2) check if something in your original prompt is causing the error, (3) explicitly say "Do NOT do X" — negative constraints can be powerful. If it persists after 3 corrections, the model may not be capable of the specific task. Switch models.
Can I recover from AI code that has bugs?
Yes, and AI is actually quite good at debugging its own code when told what the error is. Share the exact error message and say: "This code produces [error]. Fix the bug without rewriting the entire thing." AI is better at fixing code than writing it from scratch most of the time.
Is it worth saving bad AI outputs?
Yes. Bad outputs with your corrections create a "what not to do" reference. Over time, these become prompt improvement patterns. The most effective AI users keep a running log of failures and fixes — this becomes a personal playbook for avoiding common pitfalls.
How do I know if the AI output is wrong when I do not know the subject?
Three signals: (1) overly specific numbers without sources — "a 37.2% increase" cited from nowhere, (2) uniform confidence — real experts express uncertainty on some points, (3) internal contradictions — the AI says one thing in paragraph 2 and the opposite in paragraph 5. When in doubt, run the key claims through Perplexity or Google Scholar.