Production AI Integration
AI features earn trust when they are grounded, guarded, and measurable. I integrate LLMs into real products — RAG over your own data, guardrails against bad output, and evaluation — so AI ships as a dependable feature, not a demo.
Ground it in your data
Retrieval-augmented generation (RAG) connects the model to your knowledge base so answers cite real sources instead of hallucinating. The hard part is retrieval quality: chunking, embeddings, and ranking that match how your teams actually ask questions.
Guardrails before generation
Prompt guardrails, input validation, output schemas, and fallbacks keep AI features safe and predictable. You decide what the model is allowed to do, and the system fails closed when confidence is low.
Measure, then trust
Evaluation sets, human review, and monitoring of latency and cost turn "it seems to work" into evidence. AI integration is an engineering discipline: version prompts, track quality, and improve deliberately.
Related articles
Let's talk about how I can help.