The Benchmark Problem
When a new model releases, the announcement leads with benchmark scores: MMLU, HumanEval, GSM8K, and a dozen others. These benchmarks are useful for comparing models across general capabilities. They are often poor predictors of performance on your specific task. A model that scores highly on math reasoning benchmarks may behave worse than a smaller model on your specific customer support use case. Building your own evaluation is the only way to know which model is right for you.
Start With Your Task Distribution
The foundation of a useful evaluation is a representative sample of the actual inputs your application receives. If you are pre-launch, this means synthetic examples that capture the full range of what real users will ask — including edge cases, ambiguous inputs, and adversarial queries. If you are post-launch, this means sampling from real production traffic, weighted toward the hard cases where current outputs are weak.
The sample needs to be large enough to be statistically meaningful but small enough to review manually. For most applications, 50-200 carefully curated examples is more useful than 5,000 auto-generated ones. Quality of the evaluation set matters more than quantity.
Define What Good Looks Like
For each example, you need a ground truth or evaluation criterion. For factual tasks, this is straightforward: does the answer contain the correct information? For tasks with multiple valid outputs — summarization, creative writing, open-ended Q&A — you need rubrics that capture the dimensions of quality that matter for your use case.
Automated metrics (ROUGE, BLEU, semantic similarity scores) are useful for scale but miss important quality dimensions. Human evaluation is expensive but often necessary for the hard cases. A practical approach is using a capable LLM as a judge — providing it with the question, the candidate output, and a rubric — which gives you scalable evaluation at reasonable quality for many task types.
Evaluating Across Multiple Dimensions
Most applications care about multiple quality dimensions simultaneously: accuracy, format compliance, tone, conciseness, safety. Build your evaluation to score each dimension separately rather than collapsing to a single score. This lets you understand tradeoffs — a model that is slightly less accurate but much more concise may be better for your use case even if its overall score is lower.
Making the Evaluation Repeatable
The evaluation framework only creates value if it runs consistently over time. Automate it: every time you change a prompt, update a model version, or modify your pipeline, the evaluation should run automatically and produce a report you can compare to the baseline. The teams that improve their LLM applications fastest are the ones that have closed the measurement loop — they know quickly whether a change made things better or worse, so they can iterate rapidly.
