Getting Started with RAG

Retrieval-Augmented Generation (RAG) has become one of the most practical ways to enhance Large Language Models (LLMs) with your organization's specific knowledge. Unlike fine-tuning, RAG allows you to keep your data up-to-date and maintain control over what information the model can access.

What is RAG?

RAG combines two powerful capabilities:

1. Retrieval: Finding relevant documents from your knowledge base
2. Generation: Using an LLM to generate answers based on those documents

This approach solves many of the limitations of traditional LLMs, including hallucinations and outdated information.

The RAG Architecture

A typical RAG system consists of:

• Document Store: Your knowledge base (PDFs, docs, databases)
• Embedding Model: Converts text to vector representations
• Vector Database: Stores and searches embeddings efficiently
• LLM: Generates responses based on retrieved context

Implementation Steps

1. Prepare Your Documents

Start by gathering and cleaning your source documents. This includes:

• Removing irrelevant content
• Splitting into appropriate chunks
• Adding metadata for filtering

2. Create Embeddings

Use an embedding model to convert your documents into vectors:

from openai import OpenAI

client = OpenAI()
response = client.embeddings.create(
    input="Your document text here",
    model="text-embedding-3-small"
)

3. Store in Vector Database

Popular options include Pinecone, Weaviate, and Chroma. Choose based on your scale and requirements.

4. Build the Query Pipeline

When a user asks a question:

1. Convert the question to an embedding
2. Search for similar documents
3. Pass documents + question to the LLM
4. Return the generated answer

Best Practices

• Chunk size matters: Experiment with different sizes (500-1500 tokens)
• Use metadata filtering: Improve relevance with category/date filters
• Implement reranking: Add a reranking step for better results
• Monitor and iterate: Track which queries fail and improve

Common Pitfalls

1. Too large chunks: Dilute relevance and waste context
2. Ignoring preprocessing: Garbage in, garbage out
3. No evaluation: Build metrics to measure quality

Next Steps

Ready to implement RAG in your organization? Consider starting with a pilot project focused on a specific use case, such as internal documentation search or customer support.