Shipping an LLM-powered feature without a structured evaluation process is the AI-era equivalent of shipping code without tests — it might work fine in the demo and fail in ways you didn’t anticipate in production.
Deterministic software either passes or fails a test case.
Accuracy — does the model produce correct, relevant answers against a representative, labeled…
Conversational and agentic systems add failure modes a single-turn evaluation won’t catch:…
Deterministic software either passes or fails a test case. LLM outputs vary run to run, which means evaluation has to work in terms of benchmark scores, pass rates across many trials, and graded rubrics — not a single pass/fail assertion. Teams that try to evaluate an LLM feature the way they’d unit-test a function usually end up either under-testing it or writing brittle tests that break on harmless output variation.
Conversational and agentic systems add failure modes a single-turn evaluation won’t catch: does the agent recover gracefully from an off-topic detour, does it correctly decide when to hand off to a human, does a multi-step tool-calling workflow stay coherent across ten turns instead of just the first two? Evaluation plans for these systems need multi-turn, scenario-based test suites, not just single-prompt scoring.
For AI features that carry real stakes — healthcare, financial, legal-adjacent, or anything customer-facing at scale — an independent, third-party evaluation carries more weight with customers and auditors than an internal sign-off. It’s the same logic as independent financial audit versus internal bookkeeping: both matter, but they answer different questions for different audiences.
Large enough to be statistically meaningful for your use case and to cover the realistic range of inputs, including edge cases and known failure modes — often hundreds rather than dozens of labeled examples for a production system, though a smaller curated set is a reasonable starting point.
Automated scoring (exact match, semantic similarity, rule-based checks) handles a meaningful share of cases, but subjective quality dimensions — tone, helpfulness, correctness of nuanced reasoning — still benefit from human or LLM-as-judge review calibrated against human-labeled examples.
Ideally on every change that could affect output — prompt edits, model version upgrades, RAG index updates — wired into CI/CD the same way unit tests are, rather than as a periodic manual check.