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← All questions> Cost, latency and reliability

What’s a good fallback strategy when a prompt fails?

On failure, retry with a shorter prompt, then switch models or routes (e.g., retrieval on vs. off). Use deterministic validators to decide when to retry. If still failing, escalate to human with a concise summary of what the model tried. Keep logs so you can fix root causes later.

In practice

Retries should change something meaningful—shorter context or a stricter schema—not just rerun the same prompt.

Where to start

  1. [01]Define clear retry conditions.
  2. [02]Implement a second, simpler prompt path.
  3. [03]Add model routing for stubborn cases.
  4. [04]Capture a failure report for human review.
fallback promptsprompt retrieserror handling LLM

Related questions

// 001How do I prevent repetitive outputs across runs?// 002Which analytics matter for prompts in production?// 003How do I improve internal search with prompt engineering?// 004How do you optimize prompt performance and reduce costs?

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Open source · Python