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Production-grade LLM eval sets require a domain expert to manually annotate 50 traces, implement an LLM-as-judge with human validation, and establish instrumentation for continuous trace collection and evaluation.

An evals-first workflow prioritizes manual trace review, human-validated rubrics, and LLM-as-judge alignment to identify and close AI agent reliability gaps before making any prompt or model modifications.

Validating an LLM-as-judge involves having a domain expert manually annotate traces to create a baseline, then aligning the automated judge scoring against these human-validated rubrics to ensure measurement accuracy.

Reliable LLM evaluation requires a domain expert to manually annotate 50 production traces to build a human-validated baseline for establishing accurate LLM-as-judge rubrics.

Yes, production tracing requires instrumentation to measure agent behavior, collect traces continuously, and support ongoing evaluation to guide system design and model improvements across different domains.

An evals-first approach is not suitable for AI projects lacking production traces or when a domain expert is unavailable to manually annotate 50 traces for human-validated rubric alignment.