What is RAG?
Retrieval-Augmented Generation combines the power of large language models with your own data. Instead of relying solely on training data, a RAG system retrieves relevant documents from a knowledge base before generating a response. The result is more accurate, grounded and up-to-date answers.
Setting Up Your Environment
You will need Python 3.10 or higher, the LangChain library, an OpenAI API key and a vector database. Chroma is a good starting point for local development, while Pinecone suits production deployments requiring scale.
Building the Ingestion Pipeline
The first step is loading your documents. LangChain provides loaders for PDFs, web pages, Notion databases and dozens of other sources. Once loaded, split documents into chunks of around 500 to 1000 tokens and embed them using OpenAI embeddings or an open-source alternative like sentence-transformers.
Creating the Retrieval Chain
With documents embedded and stored, build a retrieval chain that fetches the top-k most relevant chunks for any given query. Combine this with a prompt template that instructs the LLM to answer only based on retrieved context to reduce hallucinations significantly.
Evaluating Your RAG System
Use Ragas or TruLens to measure faithfulness, answer relevance and context recall. These metrics give you an objective view of where your system is underperforming so you can iterate systematically rather than guessing.
