RAG: AI answers from your documents

Key considerations for retrieval-augmented generation

A document example

A question about a procedure leads the system to search the relevant documents. The model prepares an answer from the selected passages and can indicate sources. This does not guarantee the search found every necessary element.

Quality begins with sources

Current documents, clear titles and consistent permissions matter greatly. Contradictory versions must be managed. A search engine does not replace document governance.

What to test

Check questions with no answer, conflicting sources and documents the user must not access. Citations should point to passages actually used.

RAG or training?

RAG supplies context at the time of the request. It does not necessarily mean the model was retrained on your documents. The technical choice depends on the need, data and expected behaviour.

To explore the principle further: Microsoft Learn’s RAG overview.

Answering from your information

A general AI can explain many topics, but it does not necessarily know the procedure applicable in your business or the latest version of your offering. RAG aims to supply relevant information when answering by searching a collection of sources intended for that use.

For users, the experience can resemble asking a natural question: “How should I prepare for this service visit?” or “Where can I find the procedure for this file?” The system searches for useful elements, then prepares an answer using the retrieved context. The expected value is more direct access to organizational information. The idea is not to replace documents with an unreferenced conversation. Readers should be able to find important sources, understand the answer’s limits and continue searching when the topic needs greater detail.

Example: helping a team find a procedure

A business has guides, service notices and answers to frequently asked questions. An employee is looking for the process to follow in a specific case. With a traditional search, they browse several results and compare documents. Document assistance can help identify useful passages and prepare a more direct explanation.

The project should verify that the procedure presented applies to the requested case. An instruction may vary by department, product or period. If these differences cannot be identified in the sources, the answer may mix them together. Document preparation therefore matters as much as conversation. ProgExpert can design assisted access to your knowledge in a portal or application, with workflows adapted to users. The goal is to reduce repetitive searching and make understanding easier while preserving access to the authoritative document.

Choosing and preparing document sources

Start with content that answers recurring questions: procedures, service guides, product sheets or support documentation. Identify owners and current versions. Contradictory documents, duplicates and abandoned information complicate AI’s work as much as employees’ work.

Preparation may involve clarifying a title, separating several topics and adding missing context. A procedure titled simply “New method” is difficult to interpret without knowing its area and effective date. A well-maintained knowledge base is therefore a good starting point. Everything does not need rewriting before a first trial, but the selected collection’s limits must be recognized. The project can begin with a controlled set, then incorporate other sources once searches and answers are sufficiently useful to the people involved.

Search, synthesis and decisions: keeping roles clear

Finding a passage, summarizing it and making a decision are three different operations. RAG can contribute to the first two, but a concise answer may omit an important condition. Users should understand when they can rely on the explanation and when they need to consult the full source or request validation.

This distinction is particularly useful for procedures covering several cases. A short answer can point to the right section, then specify what still needs checking. Questions with no answer must also be included in tests. If documents do not contain the information, the system should not turn an assumption into an instruction. Good assistance can say what it found, identify what is missing and suggest whom or what to consult. This behaviour gives document search practical value even when not every situation is covered.

Connecting knowledge to agents and applications

A document-based answer can serve several interfaces: an internal assistant, support chatbot or voice agent. The same topic will not be presented identically to an experienced employee and a customer discovering the service. The document scope and explanation level therefore need to suit the audience.

ProgExpert can integrate this search into custom websites and applications. An AI agent can use authorized information to prepare a file or suggest a next step. A chatbot can explain a procedure and direct users to a form. Knowledge thus becomes a resource supporting a workflow rather than a set of files to open separately. Access permissions remain decisive: an answer must not reveal information simply because it is in the same document collection. The project must preserve distinctions between public content, internal content and information related to a specific file.

Measuring the quality of document assistance

Prepare representative questions with expected answers or the sources that should be retrieved. Include different phrasing, terms used by employees and ambiguous requests. Also test outdated information and questions whose answers do not exist within the selected scope.

Criteria can cover document relevance, faithfulness of the answer and ease of checking sources. Time savings should include reading and necessary corrections, not just how quickly text appears. Poorly covered questions then become a list of document improvements. ProgExpert can support this progression by connecting user feedback to content and search functions. The system becomes more useful when the organization maintains its knowledge and observes situations where assistance actually helps. Improvement therefore depends not only on the model chosen, but also on the quality of the document service as a whole.

Frequently asked questions about RAG

Do we need to train AI on all our documents?

RAG supplies sources at the time of the request and does not necessarily mean training the model on your documents. The approach depends on the objectives and information to use.

Does a citation guarantee the answer is correct?

No. Check that the source actually supports the answer and matches the context. A useful reference should make that check easier, not merely create an appearance of precision.

Can documents be updated?

The project should provide for updating them and removing replaced versions. Update delays and maintenance responsibilities must be defined so answers remain consistent with applicable content.