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.
Seven checks across accuracy, guardrails, bias, security and regression, with a self-scoring sheet so you know what to fix before you ship.
VVnT SeQuor · ISO 9001 & ISO 27001 certified · IAAP Organizational Member
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Book a free 30-minute gap reviewLarge 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.