52 posts tagged with "ai-engineering"

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.

The demo always works. Prompt the model with a curated example, get a clean output, ship the screenshot to the stakeholder deck. Six weeks later, the system is in front of real users, and none of the demo examples appear in production traffic.

This is the gap every LLM product team eventually crosses: the jump from "it works on my inputs" to "it works on inputs I didn't anticipate." The patterns that close that gap aren't about model selection or prompt cleverness — they're about system design. Seven patterns account for most of what separates functional prototypes from reliable production systems.

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.