Grounding AI Assistants in Company Knowledge with RAG
A general-purpose model doesn't know your SOPs. Retrieval-augmented generation is how you make an assistant answer from your documents.
The value of an internal AI assistant is not in what the underlying model knows about the world — it is in what the assistant knows about your organization. Retrieval-augmented generation (RAG) is the technique that closes that gap.
What RAG actually does
Instead of expecting a model to memorize your documents, a RAG system retrieves the relevant passages from your content at query time and provides them to the model as context. The model then answers using that specific material, and can cite where the answer came from.
The pipeline that matters
A production RAG system is mostly data engineering:
- Ingestion that parses PDFs, manuals, and records into clean, chunked text
- Embeddings and a vector store that make content searchable by meaning
- Retrieval that pulls the right passages for each question
- A generation step that answers strictly from retrieved context, with citations
Keeping it trustworthy
For enterprise use, two things matter more than model choice: citations, so staff can verify an answer against the source, and access control, so the assistant only surfaces documents a given user is allowed to see. We treat both as requirements, not enhancements.