An internal AI knowledge base: answers you can check

Start with the questions employees keep asking

Collect recurring questions from one team: finding a work instruction, the right version of a manual or an explanation of an internal procedure. Note where people currently look, the words they use and who can judge the answer.

Start with questions that approved documents can answer. Live stock levels, decisions about an individual case or promises to a customer need other information and authority. Do not quietly turn a knowledge base into a system that makes those decisions.

Are answers scattered or do documents contradict each other? Address information ownership. A new search layer cannot decide which instruction your organisation considers valid.

What does RAG mean for an AI knowledge base?

Retrieval-Augmented Generation (RAG) is an approach in which a system finds relevant information and supplies it to a language model to formulate an answer. Retrieval provides context; the language model writes an explanation. This is different from automatically training all company knowledge into a model.

A source reference enables checking but does not prove the answer accurately represents the source. Retrieval may find the wrong document, a condition may be missing or the model may draw an incorrect conclusion. Check both the retrieved passages and the answer.

A search function that simply returns suitable documents is also a valid outcome. Compare search with and without generated answers. Investigate what your existing software can already do.

Decide which source applies and who maintains it

Begin with a small, managed document collection. Record each source's subject, owner, valid version and effective date. Distinguish drafts, retired instructions and approved agreements. Decide how the system should handle uncertain validity.

A search index is a searchable representation of source material. Changing or deleting a document does not necessarily remove it immediately from every index, cache or stored conversation. Check how your chosen environment handles updates and deletion, and how to test this.

Show the source and relevant passage alongside an answer. If the source cannot be opened or does not support the conclusion, further checking is needed. The system should also be able to report that accessible information does not provide an answer.

Access permissions apply to retrieved passages too

An employee must not receive information through AI that they cannot read directly. Enforce permissions before supplying passages to the model. Do not rely solely on an instruction to “avoid confidential information”. Check what the chosen connector actually supports.

Test different roles, revoked access and changed group membership. Include source lists, document titles and earlier answers. A shared service account may be able to read more than the person asking the question.

Documents can contain unwanted instructions, known as prompt injection. Treat retrieved text as information, not permission to change rules or operate other systems. A knowledge base that only supports search does not need permission to edit orders.

Consider business data use in AI tools and read how to separate proposals from execution.

A small trial: can the team find the right instruction?

For example, use a fictional instruction for damaged deliveries. Version A refers to a general queue; the valid version B refers to the responsible team coordinator. Neither promises a replacement deadline. Have a person record the supporting passage for each question in advance.

On a narrow screen, scroll the table sideways. With a keyboard, focus the table and use the arrow keys.

Then include varied work questions using data you are permitted to process. Keep final evaluation questions separate from examples used to tune settings. A demonstration using only familiar questions says little about unfamiliar wording.

When does it help more than ordinary search?

Compare the current search process with the proposed support. Record whether the right source was found, whether the answer follows it, missing conditions and time spent searching and checking. Track unsupported answers and unintended access separately from an average assessment.

Include maintenance: updating documents, checking permissions, following up errors and retesting after changes. There is no universal document or employee count at which an AI knowledge base becomes worthwhile. The questions, source quality and maintenance effort determine the trade-off.

If one updated instruction or improved search solves the problem, start there. If several sources need to be combined, a scoped trial may help. Hosting, data use and necessary integrations must be checked for the chosen solution.

Develop a first trial with the pilot checklist. Need to transfer fields from documents into a system instead? Explore AI document processing.