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You can't prompt hallucinations away — you engineer around them. Five layers: RAG grounding, constrained output, checkable citations, a measured eval set, and confidence routing. The target isn't 0% errors — it's a known error rate with graceful failure.

Most teams overpay for LLM APIs by 5-10x. Model routing, prompt caching, and batching cut the bill by 60-90% in the projects we've optimized — usually in under two weeks of work. Here are the six levers, ranked by effort.

RAG and fine-tuning solve different problems. RAG gives the model new knowledge. Fine-tuning changes how the model behaves. Here's a practical guide to choosing — and when to combine both.

A practical engineering guide to adding AI features to products already in production. Model selection, architecture patterns, cost management, and the RAG vs. fine-tuning decision.