RAG (Retrieval-Augmented Generation) means fetching the relevant snippets of your data at query time and feeding them to the LLM as context, so answers are grounded in facts rather than hallucinated. You need it any time the model should answer from your specific documents, product knowledge base, or customer data — not from what it learned at training time. If a user could get burned by a hallucinated answer, you need RAG.

OpenAI (GPT-5, GPT-4o) is the fastest default — great tooling, good structured outputs, best-in-class function calling. Claude (Opus, Sonnet) wins when reasoning depth, long context, or careful instruction-following matter — many teams pick it for support chatbots because it hallucinates less on edge cases. Open-weight models (Llama, Mistral via Ollama or a hosted inference API) make sense when data can't leave your environment or when volume makes token costs prohibitive. I benchmark on your actual data before locking in.

Four levers: (1) route cheap paths to smaller models, (2) cache responses that are likely to repeat, (3) use prompt caching where the provider supports it (Anthropic and OpenAI both do), and (4) set hard budget alerts before the bill surprises anyone. On QPDF for example, aggressive chunk selection and caching keeps typical Q&A under $0.01 per query.

Three defenses. First, grounding — retrieve the actual source and pass it as context, then prompt the model to answer strictly from that context and say "I don't know" otherwise. Second, structured outputs — force the model to return JSON with a schema so downstream code can validate before displaying. Third, an eval set — 20–50 real questions with known good answers that we run on every prompt change so regressions get caught before deploy.

Yes. Tool-use agents (via OpenAI function calling or Claude tool use) that can search a database, call an API, or execute a workflow step. Multi-step agents work well when the steps are constrained and observable — I steer clear of "give the AI arbitrary powers" architectures because they're hard to debug and expensive to run. I'll tell you honestly whether an agent is the right tool for your problem or if a simpler pipeline works better.

Yes, and this is often faster than a new build. I can add AI features into existing Next.js, React, Django, Rails, or Node.js apps. Common integrations: an AI copilot inside a dashboard, a support chatbot on a marketing site, document upload and analysis in a SaaS product, or an internal tool for your ops team.

For sensitive data (healthcare, legal, financial), I use OpenAI's enterprise data policy (no training on your data), or Anthropic Claude (same guarantee), or a self-hosted open-weight model when data absolutely can't leave your environment. I handle PII redaction before sending prompts, log responses without user identifiers, and set up the auth/access patterns that HIPAA-adjacent work needs. I'm not a compliance officer — I'll flag when you need one — but I know how to build the technical layer correctly.

Because we agree on the definition of "good" before I write code. In week one I put together a small eval set of 10–50 real examples with expected outputs, and every prompt or model change gets scored against it. You see the numbers move — accuracy, latency, cost — with every iteration. "It seems to work" isn't a shipping standard.

Share the use case through the contact form — even a rough description works. I'll reply within 24 hours to book a free scoping call (~30 min), and within another 24 hours you'll have a written plan, a rough cost estimate, and a realistic timeline. Most AI projects can start within a week of the first email.