You Can't Ship LLM Projects Without Evaluation

Build evaluation before you build features. This isn’t optional. LLM projects without evaluation infrastructure fail in production because nobody knows if the system actually works.

Build evaluation before you build features. This isn’t optional. LLM projects without evaluation infrastructure fail in production because nobody knows if the system actually works.

The instinct is to build the agent first, test it manually, ship it, then worry about evaluation later. This produces systems that seem fine in demos and break silently in production.

LLMs are non-deterministic. You can’t eyeball quality. You need systematic evaluation or you’re deploying blind.

You test your customer service agent by asking it twenty questions. It answers well. You ship it. Three weeks later customers complain the agent gives wrong answers half the time.

What happened? Your twenty test questions didn’t cover the distribution of real customer queries. The agent performs well on questions similar to what you tested and fails on everything else.

Manual testing catches obvious failures. It misses systematic problems that only appear at scale across diverse inputs.

You need evaluation sets with hundreds or thousands of examples. Real customer queries, edge cases, adversarial inputs, common mistakes. Run your agent against all of them. Measure success rate. Track failure patterns.