Dead-Letter Queues for LLM Extraction Failures: Capture, Triage, and Replay Without Losing Trust

Short summary

A deep guide on designing dead-letter queues for LLM extraction pipelines, emphasizing that validation failures should be captured with full context—not silently dropped or blindly retried. It covers failure classification (transient, deterministic, ambiguous, version drift, policy), envelope design with trace IDs and schema versions, and retention discipline to avoid creating shadow databases. The core insight: a robust validation boundary emits actionable reasons, not just booleans.

• •Dead-letter envelopes must preserve full decision context: schema, model, prompt versions, confidence, and failure reasons
• •Failures should be classified into five types with different retry policies—blind retries waste money
• •Avoid turning DLQ into a shadow database; store references and enforce retention rules

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