How does RAG (Retrieval Augmented Generation) work in LangChain?

Great question! RAG (Retrieval Augmented Generation) works in 3 simple steps:

**Step 1 — Indexing your documents:**
Your documents (PDFs, text files) are split into chunks and converted into vector embeddings using models like OpenAI or HuggingFace. These are stored in a vector database like FAISS or Pinecone.

**Step 2 — Retrieval:**
When a user asks a question, the query is also converted to a vector. The system finds the most similar document chunks using cosine similarity.

**Step 3 — Generation:**
The retrieved chunks + user question are sent to an LLM (like GPT-4) as context. The LLM generates an answer based on YOUR documents.

**Simple LangChain example:**
```python
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS

qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever()
)
result = qa_chain.run("What is RAG?")
```

This is exactly what we teach in our AI Engineering Diploma course. Want to learn this hands-on?