RAG that answers well — and knows when to stay quiet

RAG stands for Retrieval-Augmented Generation. Instead of asking the model to answer from memory, the system searches your information first, selects the relevant passages and asks the model to write using only those. ChatGPT on its own does not know your prices or policies: ask it and it answers generically or invents. With RAG the answer comes from your documentation and can be verified because it cites the source.

Almost always RAG, for a practical reason: your information changes. Fine-tuning teaches a model to speak a certain way, not to memorize updatable facts — when prices change you would have to retrain. RAG separates knowledge from the model, so updating means reindexing. Fine-tuning makes sense for tone, format or very specific domains, and sometimes both are combined. We tell you which applies on the first call.

There is no strict minimum, but below roughly twenty FAQs with information that barely changes, RAG is over-engineering: a subscription platform solves that more cheaply. RAG starts paying off when the corpus is large, changes often, has many similar entities, or when a wrong answer carries real cost.

PDF, Word, Excel, HTML, Markdown, plain text, web pages and databases. Also scanned documents through OCR. What takes work is not the format but the structure: tables, documents with many sections and catalogs with variants need different chunking strategies, and that gets decided by looking at your actual corpus, not by default.

Four things combined: grounding constraints in the system prompt, mandatory citation of the passage backing each claim, explicit refusal when search returns nothing relevant, and groundedness measurement over a reference set to catch when the system starts drifting from sources. None of the four is sufficient alone.

It depends on how fast your information changes. For catalogs and pricing we usually schedule daily or event-driven reindexing; for policies and documentation, weekly. What matters is that ingestion is reproducible and has alerts when a source has gone too long without refreshing, because the most common failure mode is not breaking: it is going stale without anyone noticing.

Only if you want it to. We can deploy on Azure OpenAI or Amazon Bedrock inside your own subscription with the vector store in your cloud, so information never leaves your perimeter. In any configuration we use enterprise plans where API data is not used for training, and we filter personal data before sending.

Model consumption plus the vector store. For a mid-market company it usually lands between USD 150 and 900 per month depending on query volume and corpus size. Model routing — a cheap one to classify and summarize, a capable one for the final answer — and context trimming exist precisely to keep that figure predictable as usage grows.