RAG for AI Agents — Give Your Agent a Knowledge Base

RAG — Give Your Agent a Knowledge Base

Retrieval Augmented Generation (RAG) lets AI agents access external knowledge beyond their training data. Instead of hallucinating answers, agents can look up accurate, up-to-date information.

How RAG Works

1. Index — Split documents into chunks, generate embeddings, store in a vector database
2. Retrieve — When the agent gets a question, find the most relevant chunks
3. Generate — Pass the retrieved context to the LLM along with the question

Building a RAG Pipeline

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# 1. Load and index documents
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)

# 2. Create a query engine
query_engine = index.as_query_engine()

# 3. Ask questions
response = query_engine.query("What is our refund policy?")
print(response)

RAG Best Practices

• Chunk documents at 500-800 tokens with 100-token overlap
• Use hybrid search (keyword + semantic) for better retrieval
• Re-rank results before passing to the LLM
• Include metadata (source, date, section) with each chunk
• Evaluate retrieval quality separately from generation quality

When to Use RAG

Use RAG when your agent needs to answer questions about specific documents, company knowledge, or frequently updated information. It's the foundation of most enterprise AI agent deployments.

Learn More

Module 3 of our course covers RAG in depth, including advanced patterns like multi-hop retrieval, agentic RAG, and production optimization.