# Getting Started with RAG: Building Your First AI Knowledge Assistant
Retrieval-Augmented Generation (RAG) is transforming how we build AI applications. Instead of relying solely on an LLM's training data, RAG enables AI to pull relevant information from your specific documents and knowledge bases before generating responses.
## What is RAG?
RAG combines two powerful techniques: 1. **Retrieval**: Finding relevant information from a knowledge base 2. **Generation**: Using an LLM to generate responses based on that information
This approach gives you the best of both worlds: the language understanding of large models with the accuracy of your specific data.
## How RAG Works
The process has four main steps:
### 1. Document Processing First, you need to prepare your knowledge base. This involves: - Breaking documents into chunks (typically 500-1000 tokens) - Creating embeddings for each chunk - Storing embeddings in a vector database
### 2. Query Processing When a user asks a question: - Convert the question into an embedding - Search the vector database for similar chunks - Retrieve the top-k most relevant pieces of information
### 3. Context Building - Combine retrieved chunks with the user's question - Format as a prompt for the LLM - Include instructions for how to use the context
### 4. Response Generation - Send the enriched prompt to the LLM - Generate a response grounded in your data - Optionally include citations
## Building Your First RAG System
Here's a simple example using Python:
```python from openai import OpenAI import chromadb
# Initialize clients openai_client = OpenAI() vector_db = chromadb.Client()
# Create collection collection = vector_db.create_collection("knowledge_base")
# Add documents documents = ["Your document text here..."] collection.add(documents=documents, ids=["doc1"])
# Query def query_rag(question): # Retrieve relevant chunks results = collection.query(query_texts=[question], n_results=3) context = " ".join(results['documents'][0]) # Generate response response = openai_client.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "Answer based on the context provided."}, {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}"} ] ) return response.choices[0].message.content ```
## Best Practices
1. **Chunk Size**: Experiment with different chunk sizes. Smaller chunks are more precise, larger chunks provide more context.
2. **Overlap**: Include overlap between chunks to avoid losing information at boundaries.
3. **Metadata**: Store metadata with chunks (source, date, author) for better retrieval and citations.
4. **Hybrid Search**: Combine vector search with keyword search for better results.
5. **Guardrails**: Always validate that responses are grounded in retrieved context.
## Common Pitfalls
- **Hallucination**: LLMs may still generate information not in your documents. Add explicit instructions to only use provided context. - **Retrieval Quality**: Poor retrieval leads to poor responses. Test your embedding model and chunking strategy. - **Cost**: Each query requires embedding the question and calling the LLM. Implement caching for common queries.
## Next Steps
RAG is a powerful pattern that opens up many possibilities. Try building a simple document Q&A system with your own data, then expand from there.
In my next post, I'll cover advanced RAG techniques like re-ranking, query expansion, and hybrid retrieval strategies.