Building RAG (Retrieval-Augmented Generation) Systems over Enterprise Knowledge Bases

Enterprise RAG systems retrieve relevant material and provide it to a language model so answers can use an organization's knowledge. Retrieval augmented generation does not guarantee that the right document is found or that the response uses it correctly. Evaluate those two stages separately.

Prepare a useful, owned corpus

Choose a bounded knowledge need, such as approved product documentation or current internal procedures. Record an owner, revision date, intended audience and review status for each source. Exclude drafts that contradict approved policy unless the workflow explicitly needs them.

Structure retrieved passages so that a useful instruction retains its conditions, headings and source identity. A sentence detached from an exception can become misleading even when the words are copied accurately.

Microsoft's RAG overview describes grounding responses in proprietary content and identifies access control and relevant retrieval as implementation challenges. The pattern needs a maintained information system around it, not just an embedding index.

Enforce knowledge base permissions

Derive retrieval scope from the requester's verified access. The assistant should not retrieve a confidential document and then rely on a prompt to hide it in the answer.

Microsoft's security-filter pattern illustrates trimming search results using principal identifiers when built-in document authorization is unavailable. The general design question is whether permissions remain effective before content enters the model context.

Test users with different roles, removed access and documents whose permissions changed after indexing. Check source previews and citations as well as the answer text. A permitted title can still reveal restricted information.

Evaluate retrieval before polishing prose

Build representative questions with expected supporting documents. Include vocabulary used by customers rather than only the words in the documentation. Test:

• A precise factual question with a clear source.
• A question requiring two related passages.
• An outdated document competing with its replacement.
• A request outside the approved corpus.
• A user asking for a source they cannot access.

For retrieval evaluation, record whether the needed source appears and whether irrelevant material crowds it out. For generation, check that each important claim is supported and that the citation points to the relevant passage. A fluent answer is not a passing result by itself.

Maintain source-grounded assistants

Require an explicit limitation when evidence is insufficient, with a document-review or human route. Track ingestion failures, unanswered questions, unsupported claims and the delay between a source update and its searchable replacement.

Compare fine tuning versus RAG when behavior also needs adaptation, and use enterprise AI data governance to define source access and retention.

Make a passage useful without losing its meaning

An enterprise knowledge base often contains policies, tables, procedures and attachments that need different treatment. Split material around meaningful sections and retain the title, revision, owner and location. A procedure's prerequisites and exceptions should remain available with the action it describes. Test the extracted text, especially when PDFs contain columns or tables whose reading order can become confusing.

Avoid assuming that a larger retrieved context is always better. Extra passages can introduce outdated guidance or distract from the relevant condition. Inspect both missing evidence and unnecessary evidence when tuning retrieval. The objective is sufficient authorized support for the question, with enough context to interpret it correctly.

Separate authentication from retrieval filters

The application needs a trustworthy identity before deciding what sources the requester may receive. A user-provided department name is not sufficient proof of membership. Derive the relevant user or group context through the organization's access system, then apply the appropriate document restriction before constructing model context.

Microsoft's linked security-filter example is a filtering pattern, not authentication by itself. Treat that distinction as an implementation review question: who verifies the identity, where are permissions enforced and what happens if permission lookup fails? Check that citations, previews, cached answers and export functions honor the same access boundary as the retrieved text.

Account for permission changes after indexing. A document owner can revoke a team member's access while a search copy or cached response still exists. Define the expected update path and test it. If access freshness cannot be established for a restricted collection, use a defined unavailable response or human route rather than assuming the previous permission remains valid.

Hypothetical example: two expense-policy revisions

Suppose staff ask whether a particular travel expense is reimbursable. The collection contains last year's policy and a current revision with different approval requirements. A useful test question identifies the employee's relevant region and the policy date, with the expected supporting section specified by the policy owner.

If the assistant cites last year's document, investigate source status and retrieval before editing the prose prompt. If the correct section is retrieved but its exception is omitted, investigate passage construction and answer evaluation. If neither source covers the expense, the assistant should explain the gap and refer the question to the policy owner. This hypothetical case demonstrates failure diagnosis without suggesting a measured accuracy result.

Operate the collection as a maintained service

Give source owners a visible route to correct, replace and withdraw documents. Track ingestion outcomes so a successful file upload is not mistaken for searchable publication. After an update, run a known acceptance question against the new material and verify that superseded passages no longer drive current answers where they should not.

Keep a version record linking the source collection, retrieval settings and answer prompt used during evaluation. When unsupported answers appear, inspect the retrieved passages and access context first. Preserve enough evidence to reproduce the issue while avoiding unnecessary retention of sensitive content. Access to evaluation logs needs its own review.

Define recovery for both knowledge and service failures. An unavailable index should not silently switch to unsupported answers from model memory for internal policy questions. Restoring a previous collection may restore old permissions or obsolete guidance, so check authorization and revision status before reuse. Escalate already-issued consequential advice to the responsible business owner when correction is needed.

Start with one collection whose owner can maintain its sources and judge the acceptance questions. Expand only after retrieval, grounded answering, access changes and source withdrawal have been exercised. The result is an accountable knowledge service rather than an index whose contents nobody can confidently explain.

Scope a source-grounded assistant with SBS, starting with one owned knowledge collection and its acceptance questions.