Teams stall at the demo because they only invest in changing behavior, prompting and fine-tuningTaking a trained model and training it a bit more on your own examples so it gets better at one specific job.Full definition →, and skip the other two loops: measuring quality and inspecting traces to debug. Iteration speed is what compounds.
Three levels of evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition →, ordered by cost: assertions on every code change, model and human review on a cadence, A/B tests only after significant product changes. Clear most of level one before climbing.
LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → unit tests are just assertions, and they pay off outside the test suite. The same assertion can trigger an automatic retry at inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition →, with its error message telling the model how to course-correct.
Write assertions per feature and scenario rather than in general. A listing-search feature splits into one-result, many-results, and no-results cases, each with a concrete check like len(listings) == 0.
Generate test inputs synthetically instead of waiting for production data: ask an LLM for 50 paired instructions, one that creates a contact and one that looks it up, so the round trip itself is the assertion.
Terms in this piece · Glossary
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
This is the reference playbook for eval systems — the single highest-leverage practice separating LLM products that improve past the demo stage from ones that stall.
Key quotes
“I’ve found that unsuccessful products almost always share a common root cause: a failure to create robust evaluation systems.”
“If you streamline your evaluation process, all other activities become easy.”
“One signal you are writing good tests and assertions is when the model struggles to pass them - these failure modes become problems you can solve with techniques like fine-tuning later on.”
“99% of the labor involved with fine-tuning is assembling high-quality data that covers your AI product’s surface area.”
“Evaluation systems create a flywheel that allows you to iterate very quickly. It’s almost always where people get stuck when building AI products.”
