What Is RAG and How Does It Work with Business Documents?

RAG stands for retrieval-augmented generation. It is a way to give an AI system relevant information from external sources, such as your business documents, before it writes an answer.

In plain English, the system looks up information first, then uses it to respond. Rather than relying only on what a large language model learned during training, it can consult a selected collection of records. RAG does not require retraining that model on your archive. AWS explains the basic approach.

For a business with scanned files, policies, contracts or meeting minutes, this raises a practical possibility: asking a question in everyday language and receiving an answer linked to the documents behind it. Whether that works well depends on the quality of those documents and how the system is built.

How does RAG work?

A typical document-based workflow has two parts: preparing the records and answering questions.

1. Prepare the documents

The system ingests the selected files, meaning it imports and processes their content. Digital documents may already contain readable text. Scanned pages often need optical character recognition, or OCR, to convert the words in an image into machine-readable text.

Document processing may also identify headings, tables and reading order. This structure matters when a paragraph refers to a table or an exception appears under a separate heading. Microsoft describes text and layout extraction.

For the distinction between searchable text and structured outputs, see our searchable PDF and data-extraction guide.

2. Organise the content for retrieval

Long documents are often divided into smaller sections called chunks. This helps the system retrieve the relevant passage instead of trying to send every file to the language model.

The chunks should retain useful context, such as the document title, date, version and page reference. Poorly chosen boundaries can separate a rule from its exception. An index organises this content for search. Microsoft explains indexes and grounding data.

Our document naming and indexing guide covers the stable references that help people identify and check source records.

3. Find relevant information

There is more than one way to retrieve it:

• Keyword search looks for matching words and terms, useful for identifiers and exact phrases.
• Vector search uses embeddings, numerical representations of content, to find passages with related meaning even when the wording differs.
• Hybrid search combines keyword and vector search.

Embeddings are therefore one retrieval technique, not the definition of RAG. The right approach depends on the questions and records. Microsoft outlines these retrieval techniques.

4. Generate an answer with sources

The application passes the question and retrieved passages to a large language model, or LLM. The model uses that context to draft a response.

A well-designed interface should show citations linking the answer to its supporting documents or pages. These let a person check the evidence. Citations need to be implemented and tested; they are not proof that every statement is correct. Microsoft describes the retrieve, augment and generate sequence.

An example from a strata archive

Imagine a strata manager asking:

“Which meeting approved the roof repairs, and what spending limit was agreed?”

This is an illustrative scenario, not a Pexatech customer case study.

A suitably configured RAG system could search the authorised meeting minutes and related records, retrieve the relevant resolution, and summarise it with a page citation. If it also finds a later amendment, it should distinguish the original approval from the updated decision.

If the archive contains only a contractor's quotation, the system should not treat that as approval. If it cannot find sufficient evidence, a useful response would say so and show what it did find.

The same pattern can apply to questions about internal procedures, equipment manuals or historical correspondence. It helps people locate evidence; it does not replace their responsibility to interpret it.

Why do scanning and OCR quality matter?

An archive can look readable to a person while containing inaccurate extracted text. Scan quality, resolution, contrast and rotation can affect OCR results. Microsoft documents these limitations.

In a document-search workflow, the practical consequence is straightforward: a misread reference number can make a passage harder to find, while a misread amount can appear in an answer. Missing pages and broken table structure can remove essential context.

Before adding AI, check representative records, especially small print, tables, handwritten notes and older scans. Keep the original page available for verification. Improving the source material may be more valuable than changing the chatbot.

How does RAG compare with chatbots, search and fine-tuning?

A chatbot without access to your documents cannot reliably answer questions about your private archive. Some chat products already include retrieval features, so “chatbot” and “RAG” are not competing categories.

Traditional document search primarily returns matching files or passages. RAG adds a generated explanation based on retrieved information. Sometimes a good search result is all a user needs. Our guide to searchable PDFs in SharePoint explains the checks needed for ordinary archive search.

Fine-tuning changes a model through additional training. RAG supplies information when a question is asked. They can be combined, but keeping a document collection available for retrieval does not itself train the language model. AWS compares RAG and fine-tuning.

Permissions and reliability need deliberate design

RAG is not automatically secure. A system should check which documents each user may access before returning their content to the model or user. Adding a chat interface must not give someone access to another client's records. Microsoft explains document-level access controls.

Reliability also needs testing. Can the system find the right passage? Does its answer stay within the evidence? Does it acknowledge missing information? These are different checks, not a single accuracy claim. Microsoft distinguishes retrieval and answer evaluation.

Frequently asked questions

Can RAG work with scanned PDFs?

Yes, provided the workflow can extract usable content from them. For a text-based system, this usually means OCR followed by checks on text quality and document structure.

Does RAG prevent hallucinations?

No. Retrieved evidence can help, but the system can retrieve the wrong material, miss an important passage or generate an unsupported statement. Check source documents before relying on consequential answers.

Do new documents require model retraining?

Usually not for a RAG workflow. New or revised documents must be processed and made available to retrieval. Updates are only reflected once the relevant pipeline has completed.

Where should a business start?

Choose a small document collection and real questions people regularly ask. Test search quality, source links, access permissions and failure cases before expanding.

Start with documents people can trust

For businesses considering RAG, document scanning, OCR and sensible indexing provide an important foundation. Custom software can then connect retrieval to a suitable workflow, subject to the organisation's requirements and testing.

If you are planning to digitise an archive, talk to Pexatech about your documents and the questions your team needs to answer. That is a practical starting point for deciding whether searchable files are enough or whether a separately scoped AI-assisted workflow is worth exploring.

Read about custom AI document search and RAG development to understand the separately scoped service.