RAG & Knowledge

RAG Explained: How AI Agents Use Your Company's Data

Retrieval-Augmented Generation lets AI agents pull real answers from your documents and systems. Here's how it works in practice.

Algoritmo Lab · 8 min read · November 2025

Imagine asking ChatGPT about your company's return policy. It might give you a confident, well-structured answer — but the answer will be wrong. It will be a plausible-sounding guess based on what return policies generally look like, not what your specific policy actually says. It does not know that you extended your return window to 60 days last quarter, that exchanges require a manager override for items over $500, or that your warranty policy changed in March.

This is the fundamental limitation of large language models on their own. They are trained on vast amounts of public data, which makes them remarkably capable at understanding language, reasoning, and generating text. But they know nothing about your company's specific data — your policies, your products, your customers, your internal processes. And when they do not know something, they do not say "I don't know." They make something up. That is called hallucination, and it is the number one reason businesses hesitate to deploy AI.

Retrieval-Augmented Generation — or RAG — solves this problem. It is the single most important technique for making AI actually useful inside a business, and understanding it does not require a computer science degree. Let us break it down.

The Library Analogy

The easiest way to understand RAG is with a simple analogy. Think about what happens when you hire a brilliant new employee. On their first day, they are intelligent, articulate, and great at reasoning — but they have never read your company handbook, your product documentation, or your internal wiki. If a customer asks them a detailed question about your pricing tiers, they will have to guess. They might sound confident, but they will get the details wrong.

That is a large language model without RAG: an intelligent person who has never read your documents.

Now imagine giving that same employee full access to your company library. Before answering any question, they walk to the relevant shelf, pull out the right document, read the relevant section, and then answer the question based on what they just read. They still use their intelligence to understand the question and formulate a clear answer — but the facts come from your actual documents, not from their general knowledge.

That is a large language model with RAG: the same intelligent person, now with access to your library. The intelligence stays the same. The accuracy transforms completely. They are no longer guessing — they are citing your actual data.

How RAG Works — Four Steps

Under the hood, RAG follows four steps every time someone asks a question. Each step is straightforward, and together they create a system that is dramatically more accurate than a standalone language model.

Step 1: Ingest — Turn Your Documents into Searchable Data

First, your company's documents — PDFs, web pages, Notion wikis, Confluence articles, Google Docs, Slack transcripts, database records — are broken into smaller chunks and converted into numerical representations called embeddings. An embedding is essentially a list of numbers that captures the meaning of a text passage. Two passages about similar topics will have similar embeddings, even if they use completely different words. This is what makes semantic search possible: the system finds information based on meaning, not just keyword matching.

Step 2: Store — Save Embeddings in a Vector Database

Those embeddings are stored in a specialised database called a vector database. Think of it as a library catalogue that organises information by meaning rather than by title or author. When a question comes in, the vector database can instantly find the most relevant document chunks — even if they do not contain the exact words used in the question. Popular vector databases include Pinecone, Weaviate, Qdrant, and pgvector (an extension for PostgreSQL).

Step 3: Retrieve — Find the Most Relevant Information

When a user asks a question, the system converts that question into an embedding using the same process. It then performs a similarity search against the vector database, finding the document chunks whose embeddings are closest in meaning to the question. This typically returns the top five to ten most relevant passages. The beauty of this approach is that asking "What is our refund policy?" will match a document titled "Returns and Exchange Guidelines" even though the words are different — because the meaning is the same.

Step 4: Generate — Let the AI Answer Using Your Data

Finally, the retrieved document chunks are inserted into the prompt alongside the user's question. The language model receives something like: "Based on the following company documents, answer this question." The model uses its language understanding to synthesise the retrieved information into a clear, natural-language answer. Because the model is working from your actual data, the answer is grounded in fact rather than fabricated from general knowledge.

See It in Action

Let us walk through two concrete examples to see the difference RAG makes in practice.

Example 1: Customer Support Agent

A customer emails: "I bought a blender two months ago and the motor stopped working. Can I return it?"

Without RAG, the AI might respond: "Most retailers accept returns within 30 days of purchase. You may want to check the store's return policy for details." This is generic and unhelpful — it does not reflect your actual policy.

With RAG, the system retrieves your actual return policy document, which states that appliances carry a 12-month warranty covering motor defects. The AI responds: "Your blender is covered under our 12-month appliance warranty. Since you purchased it two months ago, you are fully covered. Motor defects are specifically included. I can initiate a warranty replacement for you right now — would you prefer a replacement or a store credit?" This answer is specific, accurate, and actionable because it comes from your actual documentation.

Example 2: Internal Knowledge Agent

An employee in the finance team asks: "What is the approval process for purchases over $10,000?"

Without RAG, the AI might give a generic answer about corporate purchasing best practices that has nothing to do with your company's actual process.

With RAG, the system retrieves your procurement policy from the internal wiki. The AI responds: "Purchases over $10,000 require three steps: (1) Submit a purchase request in the procurement portal with a business justification, (2) obtain approval from your department head and the Finance Director, and (3) for any purchase over $25,000, the CFO must also sign off. Typical turnaround is 3-5 business days. I can send you the procurement portal link if you'd like to get started." Every detail in this answer comes directly from the retrieved document.

RAG vs Fine-Tuning: Which Should You Use?

A common question we hear is: "Should we use RAG or fine-tune a model on our data?" These are different tools for different jobs, and understanding the distinction will save you months of wasted effort. Here is a direct comparison.

For most business use cases, RAG is the right starting point. It is faster to deploy, easier to maintain, and keeps your data fresh without retraining. Fine-tuning makes sense when you need the model to adopt a specific communication style, understand highly specialised terminology, or perform a task that requires deep domain expertise beyond what prompting and retrieval can achieve. In practice, many production systems combine both: a fine-tuned model that speaks your language, augmented with RAG to ensure every answer is grounded in current data.

Frequently Asked Questions

What exactly is RAG?

RAG stands for Retrieval-Augmented Generation. It is an architecture pattern where an AI system retrieves relevant information from a knowledge base before generating a response. Instead of relying solely on what the language model learned during training, RAG gives the model access to your specific, up-to-date information at the moment it needs to answer a question. The "retrieval" happens automatically — the user simply asks a question, and the system finds the right documents behind the scenes.

What is a vector database?

A vector database is a specialised database designed to store and search embeddings — the numerical representations of text that capture meaning. Unlike a traditional database where you search by exact keywords, a vector database lets you search by meaning. It answers the question "which stored documents are most similar in meaning to this query?" in milliseconds, even across millions of documents. Think of it as a library catalogue that understands synonyms, context, and concepts — not just exact words.

Is RAG secure? Can I use it with sensitive company data?

Yes, RAG can be deployed securely. Your documents stay in your own infrastructure — the vector database runs in your cloud environment or on premises. The language model only sees document chunks that are relevant to a specific query, and you control exactly which documents are indexed and who can access them. You can implement role-based access control so that an employee only retrieves documents they are authorised to see. For highly regulated industries, you can use private model deployments (like Azure OpenAI) where your data never leaves your environment and is never used for model training.

Should I use RAG or fine-tuning?

Start with RAG. For the vast majority of business use cases — customer support, internal knowledge bases, policy assistants, document Q&A — RAG delivers better results with less effort and cost. Fine-tuning is the right choice when you need the model to learn a new skill or communication style, not when you need it to know specific facts. If your goal is "answer questions about our data accurately," RAG is the answer. If your goal is "write in our brand voice while answering questions about our data," you might want both.

How much data do I need to get started?

RAG works with surprisingly little data. You can start with a handful of documents — your FAQ page, your product documentation, your company handbook — and expand from there. There is no minimum threshold the way there is with fine-tuning (which typically requires thousands of examples). A basic RAG system with 50-100 documents can be deployed in days and will already dramatically outperform a standalone language model for your specific use case. As you add more documents, the system gets better — but even a small knowledge base provides enormous value over no knowledge base at all.