How to Use AI to Build a Knowledge Base From Your Internal Documents

An AI knowledge base combines a document repository with an AI layer that can understand the content of the documents and answer questions based on them. Rather than returning a list of potentially relevant documents (as traditional keyword search does), the AI reads the relevant documents and synthesises an answer to your specific question.

The technical approach is RAG (Retrieval-Augmented Generation): the AI retrieves the most relevant document sections based on semantic similarity to the question, then generates an answer grounded in those sections. The AI can cite its sources, showing which specific documents supported each part of its answer.

This approach is fundamentally more useful than keyword search for knowledge work: 'What is our policy on approving IT vendor contracts over £100,000?' returns a synthesised answer with the relevant policy reference, rather than returning five documents that the user has to read to find the answer.

For organisations on Microsoft 365, Microsoft 365 Copilot is already a knowledge base over your organisational content: emails, Teams conversations, SharePoint documents, and meeting recordings.

Copilot's value as a knowledge base is limited by the quality and organisation of the underlying content. If your SharePoint is disorganised, if key policies are in personal drives rather than SharePoint, or if important decisions are in emails rather than in SharePoint documents, Copilot's knowledge base capability is correspondingly limited.

To improve Copilot's knowledge base performance: ensure policies, procedures, and important documents are stored in SharePoint with appropriate permissions; use clear, descriptive titles and metadata so Copilot can identify documents by topic; and archive or remove outdated versions that would produce confusing responses.

Copilot's knowledge base capability works across your full Microsoft 365 content, but only for users who have the appropriate permissions. This is a feature (sensitive documents are only returned to authorised users) and a governance requirement (ensure permissions reflect your intended access controls).

For more specific, controlled knowledge base requirements, Azure AI Search combined with Azure OpenAI provides the infrastructure for a dedicated organisational knowledge base.

This approach is appropriate when: the knowledge base needs to cover specific, curated content rather than all of an organisation's Microsoft 365 content; the knowledge base needs to be accessible to a specific user group with specific permissions; or the knowledge base requires specific AI behaviour (citing sources, following specific response formats, applying specific governance rules).

A non-technical overview of how it works: documents are uploaded to Azure AI Search, which creates embeddings and indexes them. Azure OpenAI (GPT-4) is configured to answer questions using the indexed documents as its grounding source. A front-end (which can be a Copilot Studio AI assistant, a SharePoint page, or a Teams app) provides the user interface. IT or a Microsoft partner handles the technical build; the business decision is what to include and how to govern it.

The investment is significantly higher than using built-in Copilot features, justified when the use case requires the specific controls and content curation this approach enables.

An AI knowledge base is only as good as the content it draws on. Content governance is therefore the critical enabler of AI knowledge base quality, not the AI technology itself.

For any knowledge base initiative, establish: who is responsible for creating and maintaining content in each topic area; how often content is reviewed and updated; how outdated content is identified and archived; and how the accuracy of AI answers is monitored and corrected when errors are found.

A common failure mode: an organisation invests in an AI knowledge base, populates it with existing documents, gets good initial results, and then does not maintain the content. Six months later, answers are based on outdated policies, replaced procedures, and superseded guidance. Users lose trust; the system falls out of use.

Content governance for an AI knowledge base is not a one-time project; it is an ongoing operational responsibility. Build the maintenance process before building the knowledge base.