AI features in your product

RAG development and LLM integration means building large language model features that work on your real data and are measured before they ship: search and question-answering over your documents (retrieval-augmented generation, or RAG), structured extraction, classification, drafting and summarizing, streamed into your product's interface. It is for product teams with document collections, support histories or knowledge bases, and for teams whose prototype worked in a demo and failed on real inputs.

Every engagement starts with a one-week, fixed-price scoping sprint, so the idea is tested on your data before anyone commits to a build.

In the scoping week I build a working spike against a sample of your real documents and run it on questions your users actually ask, collected with you on the first day. The report gives the approach, the measured quality on that set, the cost per request, the latency, and the risks, including what the feature should refuse to do. If the numbers say the feature isn't ready, the report says that too.

The build covers the retrieval pipeline, the prompts, structured outputs that your code can check, and a streaming interface. Answers are grounded in retrieved passages and cite them, and when the evidence isn't there the feature says so or hands over to a person.

Quality is measured, not felt. The evaluation set from the scoping sprint grows into a golden set that runs on every change: did retrieval find the right passages, is every claim in the answer supported by them, did the answer address the question. Exact checks are used where they're possible, model-graded checks where they're not, calibrated against human judgment. Guardrails against prompt injection and data leakage are tested the same way. Cost controls, caching, routing simple steps to smaller models and usage limits, are measured against the same scores so savings don't quietly lower quality. The method is written up in RAG evaluation sets: what goes in and how to score them, and LLM evals, guardrails and cost control applies it to a feature already in production.

The same discipline ran the Zyrobe pipeline, where cost per image was a design constraint from the first day and an upload became a finished product image in 3–8 s on average.

It is not model training: I integrate and deploy models, and build the retrieval, evaluation and safety layers around them. It is not suited to features where a wrong answer is dangerous and no person can review it. And it is not a chatbot for its own sake; the scoping sprint asks first whether a simpler search or a form would serve users better.