Building RAG Applications: A Practical Guide

RAG (Retrieval Augmented Generation) is revolutionizing how we build AI applications. Here's everything you need to know.

What is RAG?

RAG combines the power of large language models with external knowledge bases...

Key Components

1. Vector Database (Pinecone, Weaviate, etc.)

2. Embedding Model

3. Large Language Model

4. Retrieval Logic

Implementation Steps

Step 1: Prepare Your Data

Clean and chunk your documents for optimal retrieval...

Step 2: Generate Embeddings

Convert text into vector representations...

Step 3: Store in Vector Database

Index your embeddings for fast similarity search...

Step 4: Build the Query Pipeline

Combine retrieval with generation for accurate responses...

Best Practices

Check out our podcast episodes on RAG and vector databases for more in-depth discussions!