What Is RAG? Retrieval-Augmented Generation Explained

This article is written in English. Our training modules are available in French.

Ever wished you could give an AI access to your company's documents, your notes, or the latest information—and have it answer questions based on that specific knowledge? That's exactly what RAG does.

The Problem RAG Solves

Large Language Models have a fundamental limitation: they only know what they were trained on. This means:

• →Knowledge cutoff: They don't know events after their training date
• →No access to private data: They can't read your documents or databases
• →Hallucinations: They sometimes make up facts that sound plausible

RAG solves all three problems.

What Is RAG?

RAG stands for Retrieval-Augmented Generation. It's a technique that combines:

1. →Retrieval: Finding relevant information from a knowledge source
2. →Augmentation: Adding that information to the AI's prompt
3. →Generation: Having the AI generate a response using both its training AND the retrieved context

Think of it as giving the AI a reference library it can consult before answering.

How RAG Works (Simplified)

Step 1: Index Your Documents

Your documents are converted into embeddings—numerical representations that capture meaning. These are stored in a vector database.

Step 2: User Asks a Question

When a user asks something, their question is also converted to an embedding.

Step 3: Retrieve Relevant Chunks

The system finds document chunks whose embeddings are similar to the question's embedding. These are the most relevant pieces of information.

Step 4: Augment the Prompt

The retrieved chunks are added to the prompt as context:

Based on the following context, answer the question.

Context:
[Retrieved document chunks here]

Question: [User's question]

Step 5: Generate Response

The AI generates an answer using both its general knowledge AND the specific context provided.

RAG vs. Fine-Tuning

| Approach | What It Does | Best For | |----------|--------------|----------| | RAG | Adds external knowledge at query time | Dynamic data, private docs, citations | | Fine-Tuning | Trains model on new data | Style, behavior, specialized tasks |

RAG is often easier, cheaper, and more flexible than fine-tuning. You can update your knowledge base without retraining the model.

Why RAG Matters

1. Accuracy with Sources

RAG can cite where information came from. The AI isn't just making things up—it's referencing your actual documents.

2. Up-to-Date Information

Your knowledge base can be updated anytime. The AI instantly has access to the latest information.

3. Domain-Specific Knowledge

Train an AI on your company's SOPs, product docs, or specialized knowledge without expensive fine-tuning.

4. Reduced Hallucinations

When the AI has relevant context, it's less likely to fabricate answers. It has real sources to draw from.

Common RAG Use Cases

• →Customer support bots — Answering questions from product documentation
• →Internal knowledge bases — Helping employees find company information
• →Research assistants — Querying academic papers or reports
• →Legal/Medical analysis — Referencing specific documents with citations
• →Personalized tutors — Using course materials to help students

RAG Challenges

RAG isn't magic. Common challenges include:

• →Chunking strategy: How you split documents affects retrieval quality
• →Embedding quality: Poor embeddings = poor retrieval
• →Context window limits: You can only fit so much retrieved text
• →"Lost in the middle": LLMs sometimes ignore middle sections of long contexts
• →Relevance tuning: Retrieving the right chunks requires optimization

Key Takeaways

1. →RAG = Retrieval + Augmentation + Generation
2. →It gives AI access to external knowledge at query time
3. →RAG enables accurate, cited, up-to-date responses
4. →It's more flexible and cheaper than fine-tuning for many use cases
5. →Quality depends on how you chunk and retrieve documents

Ready to Build Your Own RAG System?

This article introduced the what and why of RAG. But building a production RAG system requires understanding chunking strategies, embedding models, and retrieval optimization.

In our Module 5 — RAG (Retrieval-Augmented Generation), you'll learn:

• →How to design effective chunking strategies
• →Choosing and using embedding models
• →Building and querying vector databases
• →Advanced RAG patterns: HyDE, reranking, query expansion
• →Implementing citations and source tracking

→ Explore Module 5: RAG