A technical deep-dive into setting up Retrieval-Augmented Generation systems using LangChain and vector databases.
Large Language Models (LLMs) are incredibly powerful, but they lack access to real-time or domain-specific data. Retrieval-Augmented Generation (RAG) solves this by pairing an LLM with a highly optimized vector search engine. In this guide, we walk through building a production-ready RAG pipeline. We cover document chunking strategies, embedding generation using OpenAI models, and indexing these vectors in Pinecone. Furthermore, we discuss handling prompt injection, managing token limits, and caching strategies to reduce inference costs and latency in enterprise applications.
Large Language Models (LLMs) are incredibly powerful, but they lack access to real-time or domain-specific data. Retrieval-Augmented Generation (RAG) solves this by pairing an LLM with a highly optimized vector search engine. In this guide, we walk through building a production-ready RAG pipeline. We cover document chunking strategies, embedding generation using OpenAI models, and indexing these vectors in Pinecone. Furthermore, we discuss handling prompt injection, managing token limits, and caching strategies to reduce inference costs and latency in enterprise applications.