RAG Tutorial

Retrieval-Augmented Generation

Connecting LLMs to real-time custom databases to prevent hallucinations.

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Grounding AI in Reality

RAG prevents "hallucinations" by providing models with external facts (from PDFs, Databases, or the Web) before they generate an answer.

Level 1 — The Architecture

  1. User Question: "What is our leave policy?"
  2. Retrieval: Search a vector database for relevant "Leave Policy" text.
  3. Augmentation: Add that text into the prompt.
  4. Generation: The LLM answers using the provided text.

Level 2 — Vector Databases

RAG utilizes Embeddings. Sentences are converted into numbers. We store these in databases like Pinecone or ChromaDB, which allow "Semantic Search"—finding things by meaning rather than keywords.

Level 3 — Evaluation (RAGAS)

RAG is complex, so we use metrics like Faithfulness (did the model lie about the source?) and Relevance (was the retrieved document actually useful?).

Simplified RAG Logic
# Pseudo-code for a RAG system
query = "How do I reset my password?"
docs = vector_db.search(query, k=3) # Retrieve top 3 relevant text chunks

prompt = f"Use these documents to answer: {docs}\n\nQuestion: {query}"
response = llm.generate(prompt)
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