How to Debug AI API Failures Across Multiple Models

Short summary

A practical guide to debugging AI API failures in multi-model production systems, covering failure taxonomy (auth errors, rate limits, invalid JSON, schema failures, quality degradation), request logging best practices, and fallback monitoring. The key insight is that debugging should be workflow-attributed rather than model-attributed, since a model may work for chat but fail for structured extraction. The article is conceptual without code examples.

• •Classify failures by type and workflow, not just by which model failed
• •Log operational metadata per request: model, provider, tokens, latency, retry/fallback status, cost
• •Monitor fallback frequency and quality degradation to catch silent failures before users notice

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