AI School: Fundamentals

What is RAG? Retrieval-Augmented Generation Explained

Learn how RAG makes AI chatbots actually useful for your business by connecting them to your real data. Visual explanations of how it works, why it matters, and when you need it.

See How It Works Our RAG Chatbots

The Problem RAG Solves

Why Regular AI Chatbots Give Wrong Answers

Standard AI models like ChatGPT are trained on general internet data up to a certain date. They know nothing about your specific business.

Ask them about your refund policy, product details, or company procedures and they will either refuse to answer or confidently make something up.

RAG solves this by giving the AI access to your actual documents at query time, so it can provide accurate, specific answers based on real information.

Without RAG

User: "What is your refund policy?"

AI: "I apologise, but I do not have information about your specific refund policy. Generally, companies offer..."

With RAG

User: "What is your refund policy?"

AI: "We offer full refunds within 30 days of purchase. Simply contact [email protected] with your order number..."[Source: Returns Policy FAQ]

The RAG Pipeline

How RAG Works: Step by Step

RAG connects your data to an LLM in real-time. Here is exactly what happens when a customer asks a question. Click each step to learn more.

1One-time setup

Document Ingestion

Your documents are processed and prepared

Details

PDFs, Word docs, web pages, databases, and other sources are converted into text chunks. Each chunk is small enough to be meaningful but contains complete thoughts.

Example

FAQ document → 50 chunks like "What is your refund policy? We offer full refunds within 30 days..."

2One-time setup

Vector Embedding

Text is converted to mathematical representations

Details

Each text chunk is converted into a vector (a list of numbers) that captures its meaning. Similar concepts end up with similar numbers.

Example

"refund policy" → [0.23, -0.45, 0.12, ...] (1536 numbers)

3Always running

Vector Database

Embeddings are stored for fast retrieval

Details

Vectors are stored in a specialised database (like Pinecone, Weaviate, or pgvector) optimised for finding similar vectors quickly.

Example

Database with 10,000+ indexed document chunks

4~50ms

User Query

Customer asks a question

Details

When a customer asks a question, their query is also converted to a vector using the same embedding model.

Example

"Can I get my money back?" → [0.21, -0.42, 0.15, ...]

5~100ms

Semantic Search

Find relevant documents by meaning

Details

The system finds document chunks whose vectors are most similar to the query vector. This works by meaning, not keywords.

Example

Returns: "refund policy" chunk even though user said "money back"

6~50ms

Context Injection

Retrieved text is added to the LLM prompt

Details

The relevant document chunks are inserted into the prompt sent to the LLM, giving it accurate information to reference.

Example

System: "Answer using this context: [refund policy text]" User: "Can I get my money back?"

7~500ms

LLM Response

AI generates answer using retrieved context

Details

The LLM uses the injected context to generate an accurate, grounded response. It can cite the sources it used.

Example

"Yes! Our refund policy allows full refunds within 30 days of purchase..."

Key Concept

Vector Embeddings: Meaning as Coordinates

The magic of RAG lies in vector embeddings - converting text into numbers that capture meaning.

Imagine a map where similar concepts are placed near each other. "Refund" and "money back" would be close together because they mean similar things. "Weather" would be far away because it is unrelated.

When a user asks a question, we find their "location" on this map, then look for nearby documents. This is semantic search - finding by meaning, not keywords.

Why This Matters

Keyword Search (Old Way)

Query: "money back"
Result: No match for "Refund Policy" document
Fails because different words

Semantic Search (RAG)

Query: "money back"
Result: Finds "Refund Policy" document
Works because same meaning

How Embeddings Look (Simplified)

Real embeddings have 1,536+ dimensions. Here is a simplified example showing how similar concepts cluster together:

Vector Embeddings Example

1// Simplified 3D embeddings (real ones have 1536+ dimensions)
2"text-violet-400">const embeddings = {
3  "refund policy": [0.82, 0.15, 0.73],
4  "money back":    [0.79, 0.18, 0.71],  // Similar to refund policy
5  "">return item":   [0.75, 0.22, 0.69],  // Also similar
6
7  "weather today": [0.12, 0.89, 0.23],  // Completely different area
8  "temperature":   [0.15, 0.85, 0.27],  // Near weather
9
10  "product specs": [0.45, 0.33, 0.88],  // Different cluster
11}
12
13// Distance between "refund policy" and "money back": 0.05 (very close!)
14// Distance between "refund policy" and "weather today": 1.42 (far apart)

Comparison

Without RAG vs With RAG

See the difference RAG makes for business chatbots.

Feature	Without RAG	With RAG
Knowledge Source	Training data only (static)	Your live business data
Data Freshness	Months or years old	Updated in real-time
Company-Specific Info
Accuracy on Your Content	Often wrong or generic	Highly accurate
Can Cite Sources
Hallucination Risk	High	Low (grounded in data)
Best For	General knowledge	Business-specific answers

Benefits

Why RAG is Essential for Business Chatbots

Accurate, Grounded Answers

Responses are based on your actual documentation, not made-up information.

Always Up-to-Date

Update your knowledge base and the chatbot immediately knows the new information.

Reduced Hallucination

By grounding responses in real documents, the AI is far less likely to make things up.

Source Citations

The chatbot can tell users exactly where it found the information, building trust.

Use Cases

When RAG Works Best

RAG excels when you need AI to work with specific, changing information.

Customer Support

Answer questions about your products, policies, and services accurately 24/7.

Return policiesProduct specificationsTroubleshooting guides

Internal Knowledge Base

Help employees find information across company documents, wikis, and manuals.

HR policiesTechnical documentationProcess guides

E-commerce

Product recommendations, stock queries, and order information from your database.

Product comparisonsAvailability checksOrder tracking

Legal & Compliance

Query large document sets to find relevant policies, contracts, or regulations.

Contract searchPolicy lookupCompliance checks

Technical Details

Key Technical Considerations

Building a good RAG system requires attention to these details. We handle all of this for you.

Chunk Size

How big should each document piece be? Too small loses context, too large returns irrelevant content. Typical: 200-500 words.

Overlap

Chunks should overlap slightly so important information at boundaries is not lost. Typical: 50-100 words overlap.

Top-K Retrieval

How many relevant chunks to include? More context = more accurate but slower and more expensive. Typical: 3-5 chunks.

Embedding Model

The model used to create vectors. OpenAI ada-002, Cohere embed, or open-source alternatives. Choice affects quality and cost.

We Handle the Complexity

Building production RAG systems requires expertise in embedding models, vector databases, chunk optimization, and LLM prompt engineering. Our team has built RAG systems for dozens of businesses - you do not need to become an AI expert.

FAQ

Frequently Asked Questions About RAG

What is RAG in simple terms?

RAG (Retrieval-Augmented Generation) is a technique that gives AI chatbots access to your specific data. Instead of the AI making up answers from its general training, it first searches your documents to find relevant information, then uses that information to generate an accurate response.

How is RAG different from fine-tuning an LLM?

Fine-tuning changes the AI model itself by training it on your data (expensive, slow, requires AI expertise). RAG keeps the model unchanged but gives it access to your data at query time (cheaper, faster, easy to update). RAG is better for most business use cases because you can update information instantly without retraining.

What types of documents can RAG use?

RAG can process almost any text-based content: PDFs, Word documents, web pages, CSV files, database content, emails, chat logs, and more. Some systems can also handle images and tables. The key is converting the content into searchable text chunks.

How accurate is RAG compared to a regular chatbot?

RAG significantly improves accuracy for domain-specific questions. Regular chatbots often hallucinate (make up plausible-sounding but wrong answers). RAG chatbots generate responses grounded in your actual documents, dramatically reducing errors. They can also cite sources so users can verify.

How long does it take to set up a RAG system?

A basic RAG implementation can be ready in 1-2 weeks. This includes document processing, vector database setup, and integration with an LLM. More complex implementations with multiple data sources and custom features may take 3-4 weeks.

What is a vector embedding?

A vector embedding is a way of representing text as a list of numbers (a "vector") that captures the meaning of the text. Similar meanings produce similar numbers. This lets computers find documents that are semantically similar to a question, even if they use different words.

How is semantic search different from keyword search?

Keyword search finds exact word matches. Semantic search finds meaning matches. If someone asks "Can I get my money back?", keyword search might miss documents about "refund policy". Semantic search understands these mean the same thing and finds the right document.

Is my data safe with RAG?

Yes, when implemented correctly. Your documents stay in your vector database - they are not sent to the AI model for training. The LLM only sees relevant chunks at query time. We use UK/EU data centres and encryption for all data storage.

How often should I update the RAG knowledge base?

It depends on how often your information changes. Most businesses update when policies change or new products launch. The process is simple: add new documents, regenerate embeddings, and the chatbot immediately has the new knowledge.

Can RAG work with multiple languages?

Yes. Modern embedding models support multiple languages and can even match questions in one language to documents in another. This is especially useful for global businesses with multilingual content.