LLMs in Production: 7 Lessons We Learned the Hard Way

The gap between an impressive ChatGPT demo and a reliable production AI system is enormous. After deploying LLM-powered features across multiple client products, here's what we've learned.

1. Prompts Are Code — Treat Them That Way

Version your prompts. Test them. Review them in PRs. A prompt change can break your product just as easily as a code change. We maintain prompt libraries with regression tests.

2. Latency Will Surprise You

LLM calls take 1-10 seconds. Design your UX around that. Streaming responses, optimistic UI, and background processing are essential patterns. Never make a user stare at a spinner for 8 seconds.

3. Hallucinations Are a Feature, Not a Bug

You can't eliminate hallucinations. You can reduce them with RAG (retrieval-augmented generation), constrained outputs, and validation layers. Always give users a way to verify AI-generated content.

4. Cost Adds Up Fast

At scale, token costs matter. Cache aggressively. Use smaller models for simple tasks. Route to expensive models only when necessary. A smart routing layer can cut costs 60-80%.

5. Evaluation Is the Hardest Part

How do you know if your AI is "good enough"? Build evaluation suites. Use LLM-as-judge for qualitative assessments. Track user feedback religiously. Automated evals are non-negotiable.

6. Guardrails Are Mandatory

Content filtering, output validation, rate limiting, and fallback behavior. Production AI needs safety nets. Plan for the model saying something wrong — because it will.

7. The Model Is the Easy Part

The real work is in data pipelines, embedding stores, caching, monitoring, and UX. The model is maybe 20% of the system. The other 80% is engineering.