Retrieval-Augmented Generation (RAG)

What is RAG?

Retrieval-Augmented Generation (RAG) combines the power of LLMs with external knowledge retrieval. Instead of relying only on the model’s training data, RAG retrieves relevant information and includes it in the prompt.

The Problem RAG Solves

Standard LLM:

• Limited to training data (knowledge cutoff)
• Cannot access your private documents
• May hallucinate facts
• Cannot cite sources

RAG-Enhanced LLM:

• Accesses current information
• Works with YOUR data
• Grounded in retrieved facts
• Can cite sources

How RAG Works

User Query → Retrieve Relevant Docs → Augment Prompt → Generate Response

Detailed Flow:

1. User asks a question: “What are the library’s hours?”

2. Retrieve relevant documents:

   • Convert query to embedding
   • Search vector database
   • Find most similar documents

3. Augment the prompt:

   Context: [Retrieved docs about library hours]
   
   Question: What are the library's hours?
   
   Answer based on the context:

4. Generate response:

   • LLM reads context
   • Generates accurate, grounded answer
   • Can cite specific sources

Key Components

1. Retriever

Finds relevant information:

• Embedding model (nomic-embed-text)
• Vector database (ChromaDB)
• Similarity search

2. Generator

Produces the final response:

• Chat model (Llama 3.2)
• Context-aware prompting
• Structured output

RAG Benefits

✓ Accuracy: Grounded in actual documents ✓ Current Information: Add new docs anytime ✓ Privacy: Your data stays local ✓ Citations: Can reference sources ✓ Smaller Models: External knowledge supplements model ✓ Domain-Specific: Works with specialized content

What You’ll Learn

RAG vs. Fine-Tuning

Use RAG when: You need to work with changing information or specific documents

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