148 posts tagged with "evaluation"

Most AI teams are measuring the wrong things, in the wrong way, with the wrong people involved. The typical evaluation setup looks like this: a 1-to-5 Likert scale, a handful of examples, and a junior engineer running the numbers. Then someone builds an LLM judge to automate it—and wonders why the whole thing feels broken six months later.

LLM-as-a-judge is a powerful pattern when done right. But "done right" is doing a lot of work in that sentence. This post is a concrete guide to building evaluators that correlate with real quality, catch real regressions, and survive contact with production.

Most engineers building with LLMs share a common arc: a working demo in two days, production chaos six weeks later. The technology behaves differently under real load, with real users, against real data. The lessons that emerge aren't philosophical—they're operational.

After watching teams across companies ship (and sometimes abandon) LLM-powered products, a handful of patterns appear again and again. These aren't edge cases. They're the default experience.

Most LLM demos work. Most LLM applications in production don't—at least not reliably. The gap between a compelling prototype and something that survives real user traffic is wider than any other software category I've worked with, and the failures are rarely where you expect them.

This is a guide to the parts that break: cost, consistency, composition, and evaluation. Not theory—the concrete problems that cause teams to quietly shelve projects three months after their first successful demo.

Most generative AI projects fail — not because the models are bad, but because teams make the same predictable mistakes at every layer of the stack. A 2025 industry analysis found that 42% of companies abandoned most of their AI initiatives, and 95% of generative AI pilots yielded no measurable business impact. These aren't model failures. They're engineering and product failures that teams could have avoided.

This post catalogs the pitfalls that kill AI projects most reliably — from problem selection through evaluation — with specific examples from production systems.