LLM Applications in Production: What Nobody Tells You
Language models are easy to demo and hard to productionize. Here are the problems you'll hit and how to handle them.
Getting a language model to produce an impressive demo takes an afternoon. Getting it to behave consistently in a production system takes significantly longer, and the failure modes are unlike anything in traditional software engineering.
The first issue is output instability. The same prompt can produce different outputs across calls, and the model's behavior can shift when the underlying model is updated by the provider. Applications that parse LLM output need to be defensive — always validate structure, always have a fallback, never assume format consistency.
The second issue is context management. Language models have token limits, and real applications frequently need to process more information than fits in a single context window. Retrieval-augmented generation (RAG) solves part of this by fetching relevant chunks at query time, but designing a good retrieval system — with proper chunking, embedding choice, and reranking — is non-trivial.
The third issue is evaluation. In traditional software, you write tests and they pass or fail. In LLM applications, correctness is often a matter of degree. You need an evaluation framework before you can ship confidently — a set of representative inputs with expected outputs, and a scoring mechanism that catches regressions when you change prompts or models.
The fourth issue is cost. LLM API calls add up fast. Production systems need caching for repeated queries, prompt compression strategies, and routing that sends simpler queries to smaller (cheaper) models.
None of these are unsolvable. But teams that treat an LLM integration like a standard API integration will hit each of them in production. The ones that plan for them upfront ship faster.