How to Actually Use LLMs in Production

Large language models are impressive in demos. Making them work in production is harder. Here's what we've learned from deploying LLMs for real clients.

First, accept that LLMs are non-deterministic. The same input won't always produce the same output. This makes testing harder but not impossible. You need robust evaluation pipelines and acceptance criteria, not unit tests that check for exact matches.

Prompt engineering matters more than you think. Small changes in wording can dramatically change outputs. Document your prompts, version them, and test changes carefully. Consider prompt management tools if you have many prompts to maintain.

RAG (retrieval augmented generation) is usually the right approach for business applications. Pure LLMs hallucinate. Grounding them in your actual data reduces hallucinations and makes outputs more useful. The quality of your retrieval system often matters more than which LLM you use.

Cost adds up fast. GPT-4 is great but expensive at scale. Consider smaller models for high-volume tasks. Mix models—use GPT-4 for complex reasoning and faster/cheaper models for simpler tasks. Cache aggressively.

Latency is a real constraint. Users won't wait 10 seconds for a response. Stream outputs, use smaller models when possible, and set expectations about response time. Some applications just aren't a good fit for current LLM speeds.

Finally, have a fallback plan. LLM APIs go down. Rate limits get hit. You need graceful degradation, not a broken user experience. What can you show when the AI isn't available?