You've heard that RAG can make AI actually useful for your business. But how does it actually work? This guide explains the process in plain English—no technical background needed.
Think of it like this: if ChatGPT is like asking a smart person who has never worked at your company, RAG is like giving that person a filing cabinet full of your documents and saying "read these first, then answer."
Step 1: Your Documents Are Collected
First, you gather the documents you want your AI system to know about. These can be:
- PDFs—policy documents, manuals, reports
- Word documents—procedures, guides, templates
- Web pages—your existing help centre or wiki
- Spreadsheets—product catalogues, pricing sheets
- Emails—past correspondence (with permission)
- Notion or SharePoint pages—your internal knowledge base
Business analogy: This is like gathering all the files in your office and organising them into one central library.
Step 2: Documents Are Split Into Chunks
A 100-page safety manual is too big for the AI to process at once. So the system breaks it into smaller, manageable pieces—typically paragraph-sized sections.
Business analogy: Instead of handing someone an entire encyclopaedia, you photocopy the relevant page and hand them just that.
Why this matters: Smaller chunks mean the AI can find exactly the right piece of information, rather than retrieving an entire document and hoping the answer is somewhere in there.
Step 3: Chunks Are Converted Into "Embeddings"
This is where it gets clever. Each chunk of text is converted into a set of numbers that represent its meaning. These numbers are called "embeddings."
Two pieces of text about similar topics will have similar numbers—even if they use completely different words. "Annual leave policy" and "holiday entitlements" would have similar embeddings because they mean similar things.
Business analogy: Imagine colour-coding every page in your library by topic. Pages about safety would all be red. Pages about HR would be blue. Pages about finance would be green. Now finding related pages is instant—just look for the same colour.
Step 4: The AI Finds Relevant Chunks
When someone asks a question, that question is also converted into an embedding. The system then finds the document chunks with the most similar embeddings—the ones most relevant to the question.
If an employee asks "How many sick days am I entitled to?", the system finds the chunks from your HR policy that discuss sick leave, personal leave, and carers leave—even if those documents never use the exact phrase "sick days."
Business analogy: You ask your librarian a question, and they immediately pull the three most relevant pages from the entire library and put them on your desk.
Step 5: The AI Generates an Answer
Finally, the AI reads the retrieved chunks along with the original question and generates a clear, helpful answer. Crucially, it answers based on your documents—not its general knowledge.
The response might be: "According to the Employee Handbook (Section 4.3), full-time employees are entitled to 10 days of personal/carer's leave per year, which includes sick leave. Part-time employees receive a pro-rata amount."
Business analogy: Your librarian reads the relevant pages, then explains the answer in plain English—and tells you exactly where they found it.
The magic: This entire process happens in 2-5 seconds. From question to sourced, accurate answer—faster than anyone could find the information manually.
Why This Matters for Your Business
- Accuracy. Answers come from your actual documents, not generic AI knowledge.
- Currency. Update a document, and the AI's knowledge updates too—no expensive retraining needed.
- Traceability. Every answer can cite its source, so you can verify it.
- Security. Your documents stay in your environment—they're not sent to public AI services.
- Scalability. The system handles thousands of questions simultaneously without getting tired, frustrated, or taking leave.
Want to see practical examples? Read our guide on 10 real RAG use cases for Australian businesses. Ready to understand the business case? Check our ChatGPT vs RAG comparison.