Dev iconDevJul 20, 2026 ~1 min source read

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment The Problem: Why "Vibe Checks" Fail in Production Three months ago, our team shipped a RAG-based customer support assistant.

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

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How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment The Problem:

Why "Vibe Checks" Fail in Production Three months ago, our team shipped a RAG-based customer support assistant.

By the time we caught it, 500+ users had seen hallucinated responses.

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The useful part

How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment The Problem: Why "Vibe Checks" Fail in Production Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing â€" we'd ask it questions, read the answers, and say "yeah, that looks right." Then it hit production.

How it works

  • Our test process was literally "ask 5 questions, read answers, thumbs up." What Production Evaluation Actually Needs Academic benchmarks (MMLU, HellaSwag) don't tell you if your system works for your use case.
  • By the time we caught it, 500+ users had seen hallucinated responses.
  • Pipeline â"Œâ"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â" â"Œâ"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â" â"Œâ"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"€â"...

Details worth keeping

The assistant confidently cited a policy that didn't exist. The post-mortem was brutal: we had zero automated evaluation.

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