LLM & RAG App Development

What's a realistic first AI feature for our SaaS?

A scoped RAG chatbot over your own docs or a document Q&A flow is usually the fastest path to value — narrow domain, clear success metric, measurable deflection. I help you pick one feature with a concrete eval target instead of an open-ended 'AI everywhere' rebuild.

Which LLM models and providers do you use?

Claude (Anthropic) and OpenAI are the defaults, routed by task — a strong model for hard reasoning, a cheap one for classification and extraction. For embeddings I use OpenAI or open-source models with pgvector or Pinecone. Provider choice is driven by your latency, cost, and data-residency constraints, not loyalty.

How do you stop the model from hallucinating?

Three layers: grounded RAG so answers cite retrieved context, a golden-set eval harness that catches regressions before they ship, and guardrails — prompt-injection defense, output validation, and 'I don't know' fallbacks. Hallucination becomes a measured number you can drive down, not a vibe.

What about data privacy — can it be self-hosted?

Yes. PII can be redacted before it reaches any provider, retrieval can run on your own pgvector/Postgres, and the whole pipeline can target self-hosted or open-weight models when data can't leave your infrastructure. We scope the privacy boundary before any code is written.

Can you integrate into our existing app and stack?

Yes — the AI feature drops into your current frontend and backend rather than living in a separate demo. Streaming UI, tool-use, and structured output integrate with React/Next, Node.js, or Rust services, behind your existing auth and APIs.