A customer asks about exchanging a product. A general chatbot produces a fluent answer, but it may not match your company’s policy. An enterprise chatbot needs to find relevant information in authorized company sources and use it to formulate its response. Retrieval-augmented generation, or RAG, is one way to do this. Its usefulness depends on the sources and on how you evaluate the answers.

If the problem is finding answers in manuals and company documents, RAG may be worth considering. For a live order status, you also need a connection to the order system. Identify the question and the source of the answer before choosing a tool.

What is RAG, and how does it work?

RAG stands for retrieval-augmented generation. Instead of relying solely on a model’s general knowledge, the system retrieves relevant material from an external source and provides it to the model. This usually does not require retraining the model on all your company documents. IBM’s explanation of RAG also distinguishes it from fine-tuning.

  1. You select reliable sources, such as product manuals and service policies.

  2. The system prepares their content for search; many implementations split text into smaller sections.

  3. A user asks a question, and the system retrieves relevant sections the user is authorized to access.

  4. The model drafts an answer using the question and the retrieved information.

  5. The answer can include source references so the user can check it.

This process does not necessarily turn company knowledge into permanent model memory. However, retrieved passages may be sent to a model service to generate the answer. Processing location and data retention need separate assessment.

A hypothetical example: product exchanges

Suppose a retailer has an approved document defining exchange deadlines and conditions. A customer asks, “Can I exchange an opened product?” The chatbot should retrieve the section for that product category, explain the conditions and exceptions, and provide a source link. If the product type is unclear, it should ask a follow-up question. If the document offers no clear answer, it should refer the matter to a support representative.

This is an illustrative example, not a GaliTech project or result. The important point is selecting the right document. An outdated policy or a rule for another product category can lead to an invalid answer, even when the wording sounds natural.

Which information fits RAG, and what needs a separate connection?

  • Relatively stable knowledge: service procedures, product guidance and employee training materials can serve as knowledge-base sources.

  • Frequently changing information: policy updates need to reach both the source and the search index. Connecting documents once does not guarantee freshness.

  • Live and personal data: inventory, current prices and order status usually require an authorized connection to the system of record. Responses must account for identity and access rights.

  • Taking action: placing an order or changing a customer record requires separate operational permissions and controls.

Real implementations can combine document retrieval with tool connections. RAG itself does not give a chatbot permission to modify your systems.

When might you not need RAG?

For a few simple, fixed questions, an FAQ page or predefined replies may be enough. If staff only need to locate documents, consider improving internal search. A chatbot is worth exploring when conversation, explanation or combining information across sources helps the user.

Before commissioning custom development, assess existing tools, data access and maintenance costs. You also need to decide where this capability belongs in the product. Our article on websites, apps and platforms discusses that choice.

Why can an answer still be wrong?

RAG can connect answers to relevant sources, but it does not eliminate errors. Incomplete documents, misunderstood questions, poor retrieval or faulty model reasoning can still produce a wrong answer. A citation supports verification; a link alone does not prove every claim is correct.

Identify conflicting sources, assign document owners and record validity dates. Define what happens when information is insufficient, and preserve human review for sensitive decisions. AWS documentation explains the use of retrieved information and source references.

Private documents do not automatically mean private answers

A sales employee should not obtain another department’s confidential records through the chatbot. Enforce permissions during retrieval; hiding a link in the interface is insufficient. Microsoft’s RAG documentation emphasizes access controls for source content.

Specify where data is processed, who can see conversation logs and how long data is retained. Source documents may also contain malicious or misleading instructions. OWASP’s prompt-injection guidance explains why RAG alone does not resolve this risk. Source content must not acquire authority to change permissions or execute operations.

Start with a small pilot and clear measures

A practical approach is to select one user group and one bounded problem, such as answering questions from a service manual. Organize reliable sources and build a test set containing real, ambiguous, unanswerable and unauthorized questions. Compare the results with the current process.

  • Does retrieval find the correct passage?

  • Are the answer’s claims supported by that passage, and do they address the question?

  • Does the system acknowledge insufficient information?

  • Do permissions and document updates or deletions work correctly?

  • What are response time, usage cost and human-review requirements?

These are suggested pilot criteria. Microsoft’s evaluation guidance also assesses retrieval and answer quality separately. Budget beyond model usage: document preparation, integrations, access controls and ongoing maintenance require work too.

Common questions

Is RAG the same as training a model on company data?

No. RAG retrieves information when answering. Fine-tuning changes model parameters for a specific objective. Both approaches can be used in the same solution.

Does the chatbot automatically read every file?

Only sources whose connections and permissions have been configured. Scanned files may need text extraction, and extraction quality needs checking.

Must the solution be custom-built?

No. An existing product, better search or integration with current systems may meet the need. Scope, permissions and maintenance capacity determine the choice.

Your next step

Identify three recurring questions, the sources currently used to answer them and the people authorized to view those sources. This distinguishes knowledge retrieval from live-data access or an operational task.

If you identify a use case in your company, send a description of the need and the types of information sources to info@galitech.ir for an assessment of implementation feasibility. A general description is enough for the initial contact; do not send confidential documents.