Beginner

Introduction to RAG

Retrieval Augmented Generation (RAG) is a technique that grounds AI responses in real, relevant data by retrieving information before generating an answer.

What is RAG?

Retrieval Augmented Generation (RAG) combines information retrieval with text generation. Instead of relying solely on what an LLM learned during training, RAG first searches a knowledge base for relevant documents, then feeds those documents as context to the LLM to generate an informed answer.

RAG in One Diagram 
User Question: "What is our refund policy?"

         ↓
1. RETRIEVE: Search knowledge base for relevant documents
         ↓
2. AUGMENT:  Add retrieved documents to the LLM prompt
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3. GENERATE: LLM produces answer grounded in real data
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Answer: "Our refund policy allows returns within 30 days
of purchase. Items must be in original condition..."
[Source: company-policies.pdf, page 12]

Why RAG?

Large Language Models are powerful, but they have fundamental limitations that RAG addresses:

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Reduce Hallucinations

LLMs can confidently generate incorrect information. RAG grounds answers in real documents, dramatically reducing hallucinations.

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Use Private Data

LLMs do not know your company's internal documents, policies, or data. RAG gives them access to your private knowledge base.

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Stay Current

LLM training data has a cutoff date. RAG lets you feed in up-to-date information without retraining the model.

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Provide Citations

RAG can cite exactly which documents informed the answer, making responses verifiable and trustworthy.

RAG vs Fine-Tuning vs Long Context

Approach Best For Cost Data Freshness Accuracy
RAG Knowledge-intensive Q&A, private data Medium (retrieval + generation) Real-time (update knowledge base anytime) High (grounded in source documents)
Fine-Tuning Changing model behavior/style High (training costs) Static (must retrain for updates) Medium (can still hallucinate)
Long Context Small, focused document sets High (per-token costs) Real-time (pass docs in prompt) Good (but degrades with many docs)
✅
When to use RAG: Choose RAG when you have a large knowledge base (more than fits in a context window), need citations, want real-time data updates, or must reduce hallucinations on factual questions.

The RAG Pipeline Overview

  1. Ingest

    Load documents from various sources: PDFs, web pages, databases, APIs, Slack, Notion, etc.

  2. Chunk

    Split documents into smaller pieces (chunks) that can be individually embedded and retrieved.

  3. Embed

    Convert each chunk into a vector (numerical representation) using an embedding model.

  4. Index

    Store vectors in a vector database (Pinecone, ChromaDB, Weaviate, etc.) for efficient similarity search.

  5. Retrieve

    When a user asks a question, embed the question and find the most similar chunks in the vector database.

  6. Generate

    Feed the retrieved chunks as context to the LLM along with the user's question. The LLM generates an answer grounded in the retrieved data.

Real-World RAG Applications

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Customer Support

AI chatbots that answer questions using your product documentation, FAQ, and knowledge base articles.

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Documentation Search

Search across thousands of technical documents, manuals, and guides to find precise answers.

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Legal Research

Search case law, contracts, and regulations to find relevant precedents and clauses.

☤

Medical Q&A

Answer medical questions grounded in clinical guidelines, research papers, and drug databases.

History and Evolution

RAG was introduced by Meta AI researchers in 2020 in the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Since then, the field has evolved rapidly:

  • 2020: Original RAG paper by Lewis et al. at Meta AI.
  • 2022: Vector databases (Pinecone, Weaviate) make retrieval practical at scale.
  • 2023: LangChain and LlamaIndex make RAG accessible to developers. Advanced techniques emerge (HyDE, reranking, hybrid search).
  • 2024: Production RAG becomes mainstream. Evaluation frameworks (RAGAS) help measure quality. Multi-modal RAG (text + images) emerges.
  • 2025-2026: Agentic RAG (AI decides when and how to retrieve), graph RAG, and sophisticated multi-step retrieval become standard.
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This course covers: Everything from basic RAG concepts to advanced production techniques. You will build a complete RAG system by the end of this course.

What's Next?

The next lesson covers the RAG architecture in detail — offline pipelines, online pipelines, components, and architecture patterns.

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