How Should You Evaluate Your LLM Application?
For most teams: start with LLM-as-judge for fast iteration, build a golden dataset of 50–200 hand-curated examples for regression testing, and add A/B testing in production once you have meaningful traffic. Human evaluation is the gold standard but is only cost-effective for periodic deep dives.
What type of task is your LLM application performing?
FAQ
What is LLM-as-judge and how reliable is it?+
LLM-as-judge means using a strong LLM (Claude Opus 4, GPT-4o) to score the outputs of another LLM. Studies show strong models achieve 70–85% agreement with human raters when given detailed rubrics. Known biases include: preference for longer responses, preference for outputs from the same model family, and positional bias (preferring the first option in pairwise comparisons). Use calibration techniques: random order shuffling, multi-metric rubrics, and human spot-checking.
What is a golden dataset?+
A golden dataset (or golden set) is a curated collection of inputs with known, correct, ideal outputs. These are human-reviewed and represent the full distribution of cases your system will encounter — including edge cases, ambiguous inputs, and failure modes. Golden datasets serve as regression tests: if a model or prompt change causes any golden examples to fail, you catch the regression before it reaches production.
How many examples do I need for a useful golden dataset?+
50 examples is enough to start getting useful signal. 200 examples covers most common task distributions adequately. 500+ examples gives you statistical power to detect small quality regressions. Quality and diversity matter more than quantity — 50 carefully chosen examples that cover your failure modes are worth more than 500 easy, similar examples.
What tools should I use for LLM evaluation?+
Popular options in 2026: Braintrust (best for teams, hosted, good LLM-as-judge tooling), Langsmith (tight LangChain integration, good tracing), Promptfoo (open-source, best for prompt A/B testing), RAGAS (specialized for RAG evaluation), Helicone (production monitoring + evaluation). For code, EvalPlus provides execution-based benchmarking. Many teams eventually build custom eval harnesses once they have specific domain metrics.