Building eval systems that improve your AI product

The most common mistake in AI evaluation is starting with off-the-shelf metrics like hallucination or toxicity scores, which often don't correlate with the actual problems users face.

Effective AI evaluation starts with error analysis using a single principal domain expert who reviews approximately 100 user interactions with open coding and axial coding to discover real failure modes.

For AI evaluation, binary pass/fail judgments are more effective than 1-to-5 Likert scales because the distinction between adjacent scores is subjective and inconsistent, while nuance is captured in written critiques.

In RAG systems, you should fix the retriever before investing in generator improvements, because if the correct information is not retrieved, the generator has no chance of producing a correct answer.

The real competitive advantage in AI products comes not from prompting but from building a continuous improvement flywheel where production monitoring flags failures, error analysis finds root causes, and fixes are added to a golden dataset.