RAG for business: build an AI assistant that uses your knowledge

Retrieval-augmented generation, or RAG, retrieves relevant information and supplies it to a model when answering a question. Instead of expecting the model to remember your internal policies, the application searches an approved knowledge source. This can make answers more grounded and easier to trace, but it does not guarantee that the retrieved information or the resulting answer is correct.

A useful first scope is a repeated question whose answer already exists in a reliable document. Product specifications, internal procedures and approved service descriptions can fit. Questions requiring live inventory or a customer's current balance need a system lookup as well; a document library is not a substitute for transactional data.

Inventory the material and identify duplicates, outdated versions and conflicting policies. Assign an owner to each source and record its scope, language and update date. Clear documents with descriptive headings are easier to retrieve from than a folder full of vaguely named exports. Preserve the surrounding context when splitting material into searchable sections.

Do not ingest every file simply because storage is available. Exclude information the assistant does not need, and establish a process for removing or replacing documents. If two policies disagree, resolve that disagreement with the source owner. A model should not be expected to decide which business rule is legally or operationally authoritative.

Different employees or customers may have different rights to the same library. Enforce those rights when selecting documents, not after an answer has been generated. Otherwise restricted material can influence a response even if the final text omits the document name. Source citations do not make an unauthorised disclosure acceptable.

Retrieved text is also an untrusted input. A document that says 'ignore your instructions' must not gain control over the assistant's tools. Keep retrieval separate from action permissions. Decide what logs are necessary for diagnosis and how sensitive passages will be handled; more logging is not automatically better governance.

Create a test set containing answerable questions, questions with no available answer and questions whose wording differs from the source. Check whether the system retrieves the correct passages and whether its answer stays within them. Inspect citations directly: a link can point to a real source while failing to support the sentence attached to it.

Define the expected response when evidence is insufficient. It might ask a clarifying question, state that the policy is not available or route the request to its owner. Test fresh documents and deleted ones to confirm the index updates as intended. Evaluate retrieval and generation separately so you know which layer needs repair.

Start with a narrow topic and an audience that can give informed feedback. Record unanswered questions and review whether the missing element is a source, retrieval configuration or an unclear question. Track useful resolutions rather than simply counting messages; a long conversation may indicate confusion rather than value.

A practical brief includes approved source examples, access groups, representative questions and the handover owner. My custom assistant work begins with those boundaries. RAG is valuable when it makes reliable business knowledge available at the moment of need, not when it gives a model access to the largest possible folder.