Deploying a language model to production looks deceptively simple. You call an API, you get text back, you show it to users. But production AI is nothing like playing with ChatGPT. Latency kills conversions. Failures need graceful handling. Costs balloon unexpectedly. Users expect reliability from AI, not magic.

Cost Management Is Critical

The economics of LLMs are backward from most software. You don't pay for compute—you pay per token. A user asking for a 5000-word essay costs the same whether it takes 2 seconds or 20. But streaming one token every 100ms costs more than batching and returning all at once.

Track your costs obsessively. Measure price per request, price per user, price per feature. Use smaller models where they work. Cache aggressively. Implement request batching. A user feature that costs $0.50 per request will bankrupt you before it ships.

Latency Optimization Matters

Users expect AI to be fast. A 4-second response feels slow. A 10-second response feels broken. Use streaming responses, not batching, when the user is waiting. Pre-compute what you can. Use cheaper models for summarization and classification, expensive models only for generation.

Region matters. Route requests to the nearest API endpoint. Cache popular requests. Use a queue with multiple retries for non-blocking operations.

Prompt Engineering in Production

Your prompts will drift. Users will ask questions you didn't anticipate. System prompts that worked in staging fail in production. Version your prompts. A/B test them. Track which prompts produce good outputs.

Build prompt templates, not monolithic systems prompts. Let the user's request shape the prompt. Include examples of good outputs in the prompt itself. Make it easy to iterate on production prompts without redeploying code.

Build for Failure and Distrust

AI fails gracefully, which is worse than failing obviously. A bad API call returns an error. A bad AI response returns plausible-sounding nonsense. Users trust AI outputs more than they should.

Always have a fallback. Show confidence scores. Require human review for critical decisions. Let users easily flag bad outputs so you can learn and improve. Be transparent: "This is generated by AI and may be inaccurate."

AI is powerful when it augments human decision-making. It's dangerous when it replaces it.