After building AI systems for dozens of clients over the past two years, we’ve watched the same failure patterns emerge with uncomfortable regularity. The frustrating part? It’s almost never the model’s fault. What breaks is everything around it.
The integration is the product
The most expensive mistake we see: treating the LLM as the product and the integration as infrastructure. In reality, the integration is most of what users experience. The model just predicts tokens — context management, retrieval quality, error handling, and the feedback loop are on you.
Failure pattern #1: Context starvation
Models are only as good as the context they’re given. Sending a bare user message and a short system prompt and expecting expert output is the most common mistake we see.
Failure pattern #2: Retrieval as an afterthought
RAG is the right call for most production features, but it’s an architectural decision, not a last step. Retrieval quality directly caps output quality.
How to make it stick
Start narrow. Treat prompt design as engineering. Measure output quality before shipping. And budget for iteration — AI features need refinement cycles most traditional features don’t.