Building a RAG Knowledge Assistant on SharePoint and Microsoft 365

Most organizations are sitting on years of institutional knowledge, policies, procedures, project documents, proposals, scattered across SharePoint sites and Microsoft 365. The knowledge exists; finding it is the problem. A retrieval-augmented generation (RAG) assistant fixes exactly this: it lets employees ask a plain-language question and get an accurate answer, with citations, drawn from your own documents rather than the open internet. Here is how these assistants work and how to build one responsibly.

What is a RAG knowledge assistant?

A large language model on its own is fluent but forgetful, it knows nothing about your company and will confidently invent answers when it does not know. Retrieval-augmented generation solves this by pairing the model with a search step. When a user asks a question, the system first retrieves the most relevant passages from your own content, then asks the model to generate an answer using only those passages. The result is grounded in your documents, and every answer can cite the sources it used, so people can trust and verify it.

In short: the model supplies the language, your content supplies the facts.

Why SharePoint and Microsoft 365 are ideal sources

For a Microsoft-centric business, SharePoint is often the single richest source of ground truth: it already holds your documents, has metadata and versioning, and, crucially, has permissions. That last point matters more than anything. A knowledge assistant must never reveal a document to someone who is not allowed to see it. Because SharePoint already models who can access what, it gives a RAG system the security backbone it needs.

How it works, step by step

A production RAG assistant on Microsoft 365 typically follows this pipeline:

Ingest. Documents are pulled from SharePoint and other Microsoft 365 sources.
Chunk. Each document is split into passages small enough for precise retrieval.
Embed and index. Passages are converted into vector embeddings and stored in a search index (for example, Azure AI Search), so the system can find them by meaning, not just keywords.
Retrieve. At question time, the assistant finds the passages most relevant to the query, filtered by what that specific user is permitted to see.
Generate. The model composes an answer using only the retrieved passages, and returns it with citations.

Keeping it secure and accurate

Two concerns make or break an enterprise knowledge assistant, and both are solvable:

Permission trimming. The retrieval step must respect the user’s Microsoft 365 permissions, so the assistant only ever answers from documents that user could already open. This is non-negotiable and should be designed in from day one.
Grounding and citations. By constraining the model to answer only from retrieved content, and showing its sources, you dramatically reduce hallucination and give users a way to check the answer. If the content does not contain the answer, a well-built assistant says so instead of guessing.

Build options

There is no single right way to build this; the best choice depends on how much control you need:

Copilot Studio and Microsoft 365 Copilot. For many organizations, the fastest route is to ground a Copilot Studio agent in SharePoint content. It is low-code, respects Microsoft 365 security, and gets a capable assistant live quickly.
A custom RAG solution on Azure. When you need full control over chunking, retrieval, ranking, and the user experience, a custom pipeline on Azure AI Search and Azure OpenAI is the way. It is more engineering, but it lets you tune quality precisely and integrate beyond Microsoft 365.

Many organizations start with the low-code option to prove value, then invest in a custom build once the use case is established.

At PanaceaLogics, our Microsoft-certified engineers build permission-aware knowledge assistants grounded in your SharePoint and Microsoft 365 content. Explore our SharePoint solutions and RAG knowledge assistants, or get in touch to scope a pilot.