Resser Solutions

Operations··9 min read

How to Evaluate an LLM in Production: A Practical Eval Guide

Building a real eval suite for LLM features: dataset design, metrics, CI integration, regression detection. Without slop.

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How to evaluate an LLM in production is the most-skipped engineering question in AI. A production AI feature without an eval suite is a science project , when it breaks you cannot tell why, and when it gets better you cannot tell why either.

Eval pipeline we build into every project

  1. 01Build the eval dataset from real (anonymized) production data. 100-500 examples covering common cases, edge cases, and known failure modes.
  2. 02Define metrics that match the user-facing outcome. Accuracy, faithfulness, citation correctness , never just BLEU/ROUGE in isolation.
  3. 03Run eval in CI on every prompt or model change. Regressions block merge.
  4. 04Pipe the eval results into a dashboard so non-engineers can see them.
  5. 05Re-curate the eval set quarterly. Production data shifts; the eval should too.

Tools we use

  • LangSmith , hosted traces + eval datasets, UI for non-engineers.
  • Braintrust , CI-native eval, programmable.
  • Promptfoo , CLI eval suite that blocks PRs.
  • Helicone , cost + latency observability, complements eval.
  • Custom harnesses in Python or TypeScript when the metric is domain-specific.

FAQ

Frequently asked.

How big should the eval set be?

100-200 cases for most B2B features. 500-1000 for higher-stakes systems (healthcare, fintech, legal). The number matters less than coverage , ensure common cases, edge cases, and known failure modes are all represented.

Who labels the eval set?

Domain experts on the customer's team. We help design the labeling guide, but the label decisions belong to the people who own the workflow. ML-only teams labeling business eval sets is a common failure mode.

How often should we run eval?

On every prompt change (CI). Plus weekly on a refreshed sample from production. Plus a full re-evaluation quarterly when you re-curate the dataset.

Can we use LLM-as-judge?

For some metrics, yes. For high-stakes decisions, complement LLM judges with human review on a sample. LLM judges drift with model updates; humans give you a stable anchor.

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