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Evaluating LLM Applications in Production: Beyond the Demo

Ananya RaoHead of AI ResearchApr 25, 20266 min read

Summary

A demo that works 9 times out of 10 isn't a production system. Here's how we build evaluation pipelines that catch the 10th time.

Key takeaways

How to use this in real systems

  1. 1.The gap between an impressive LLM demo and a trustworthy production system is almost entirely about evaluation. Demos get judged on best-case outputs; production systems get judged
  2. 2.We build evaluation pipelines with three components: golden datasets that reflect real usage, automated scoring using a mix of rule-based and model-graded metrics, and continuous r
  3. 3.This post shares our evaluation stack and how we've used it to catch regressions before they ever reach a customer.

Article

The gap between an impressive LLM demo and a trustworthy production system is almost entirely about evaluation. Demos get judged on best-case outputs; production systems get judged on worst-case outputs.

We build evaluation pipelines with three components: golden datasets that reflect real usage, automated scoring using a mix of rule-based and model-graded metrics, and continuous regression testing on every prompt or model change.

This post shares our evaluation stack and how we've used it to catch regressions before they ever reach a customer.

EvaluationLLMOpsQuality

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