A comprehensive guide to pre-launch testing for LLM applications. Covers defining a product contract, versioning prompts and model settings, building representative eval datasets (smoke tests, regression, edge cases, trace replay), writing rubrics tied to real failure modes, calibrating LLM-as-judge evaluators, asserting intermediate workflow behavior (retrieval, tool calls, routing), running adversarial tests, load testing for latency and cost, comparing against baselines, and setting release gates before tuning begins. Includes a practical 11-step pre-launch checklist and common mistakes to avoid.
Table of contents
Start by defining what “correct” meansVersion the prompt, model settings, and workflow before testingBuild an eval dataset that looks like productionWrite rubrics that penalize real failuresUse LLM-as-judge carefullyTest the full workflow, not the final answer onlyReplay real traces before launchSet release gates before you see the resultsRun adversarial and messy-input testsCheck latency, cost, and rate limits under realistic loadCompare against a baselineCommon mistakes to avoidA practical pre-launch checklist104 Impressions