How to Add Evals to an LLM Feature

Short summary

A practical guide to instrumenting LLM features with evaluation suites, using an AI calling agent as a case study. Covers defining success criteria as business outcomes, curating representative test datasets from real transcripts, and mixing LLM-based judges (GPT-4o) with deterministic NLI scorers using the DeepEval framework. Includes concrete code examples for metric definitions and test case structures, emphasizing that evals are the spec for non-deterministic systems.

• •Define eval success criteria as business outcomes, not technical metrics
• •Curate representative test datasets covering real edge cases and failures
• •Mix LLM judges with deterministic NLI scorers to ground evaluations
• •Automate evals in CI using DeepEval framework with code examples

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