How to Build an AI Knowledge Base on Your Company Documents

An AI knowledge base answers staff or customer questions from your own documents using retrieval-augmented generation (RAG): it finds the passages relevant to a question and has a model answer from them, with sources cited. Off-the-shelf assistants can do this for small document sets; a custom build makes sense when you need permissions, many sources kept current, or the answers on your website.

How it works

  1. Collect the source documents: policies, manuals, past proposals, help articles, and FAQs.
  2. Split and index them into passages, each stored with an embedding: a numeric representation of its meaning that allows search by meaning rather than exact words.
  3. Retrieve the passages closest to each question, usually combining meaning-based and keyword search.
  4. Answer with a language model instructed to use only those passages and to cite them.

This approach is called retrieval-augmented generation (RAG). It keeps answers grounded in your documents and lets you update the knowledge base by updating the documents, with no model training involved.

Off-the-shelf options

For a small set of documents and internal use, the business plans of ChatGPT and Claude let you upload files to a project and ask questions about them, and Microsoft 365 Copilot can answer from files your staff already have access to in SharePoint and OneDrive. Try these first.

When to build one

  • Customer-facing answers on your website or in support, where tone, scope, and citations need control.
  • Many sources kept current: a help center, a shared drive, a ticket history, and a database, re-indexed automatically as they change.
  • Permissions: different staff or clients may see different documents.
  • Actions: the assistant should also open a ticket, book a call, or look up an account.

What makes the answers trustworthy

  • Citations on every answer, linking to the source passage.
  • "I don’t know" when the documents do not cover it, instead of a guess.
  • A test set: real questions with known correct answers, rerun after every change.
  • Clean sources. Outdated or contradictory documents produce outdated or contradictory answers; retiring old versions matters more than any model setting.

We build these as a service: see AI chatbots and knowledge bases.

Frequently asked questions

What is a RAG chatbot?
A chatbot that answers by first retrieving relevant passages from your documents, then having a language model write the answer from them, with sources. RAG stands for retrieval-augmented generation.
Do we need to train a model on our documents?
No. Retrieval keeps your documents separate from the model, which is cheaper, easier to update, and lets every answer cite its source. Fine-tuning is rarely needed for question answering.
Can an AI chatbot on our website give wrong answers?
It can, which is why it should answer only from your documents, cite them, decline questions they do not cover, and be tested against real questions before and after every change.

Related