Building a RAG System from Scratch with pgvector and Gemini — Introduction

This guide dives into creating a Retrieval-Augmented Generation (RAG) system to enhance large language models (LLMs) with real-time data retrieval capabilities. Since LLMs are limited to their training data, RAG systems bridge this gap by fetching and incorporating relevant, up-to-date information from external sources. This is crucial for applications needing to stay current with company-specific data or the latest industry specs. By using tools like pgvector and Gemini, the guide walks through building a robust RAG system from the ground up, providing valuable insights for developers looking to leverage LLMs in practical, real-world scenarios.