How to run human-in-the-loop evals for LLM apps

Human-in-the-loop evaluation is a process where subject matter experts assess and score outputs from large language models (LLMs) using a predefined rubric, addressing shortcomings that automated scorers often miss, such as tone, domain-specific accuracy, and policy compliance. This evaluation is crucial in environments like healthcare, finance, and legal sectors, where manual reviews support audit trails and ensure outputs meet policy and production use case requirements. While automated evaluators can handle high-volume checks efficiently, they are prone to favoring longer, more confident answers without catching factual errors. Human reviews, therefore, focus on edge cases, factual disputes, and policy-sensitive outputs, providing insights that automated systems might miss. Braintrust offers a platform that integrates human review into the same workflow used for trace inspection, scoring, and continuous integration/deployment (CI/CD), allowing for structured feedback that enhances both evaluation and release decisions. It enables span-level scoring, ensuring that failures are accurately traced to their origins, whether in retrieval, tool use, or generation, and helps convert reviewed failures into regression tests for future deployments.

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