How to Build RAG with Spring AI and pgvector AI for developers and Programmers java RAG in AI Spring Spring AI Spring Boot by devs5003 - July 6, 2026July 22, 20260 How to Build RAG with Spring AI and pgvector: Full Tutorial 2026 Every large language model has a hard cutoff. Ask it about your company's internal onboarding policy, a document uploaded yesterday, or last week's product release, and it either admits it does not know or, worse, makes something up that sounds confident and correct. Your LLM cannot answer questions about last week's internal policy update, your product's latest release notes, or a PDF sitting in your company's document store. It only knows what it was trained on. RAG fixes that by handing the model your own data at query time, and Spring AI makes the whole pipeline buildable in plain Java. What You Will Learn A Retrieval-Augmented Generation application built with Spring AI
What Is RAG in AI? A Simple Guide for Developers 2026 AI for developers and Programmers RAG in AI by devs5003 - July 6, 2026July 22, 20260 What Is RAG in AI? A Simple Guide for Developers 2026 Have you ever asked an AI assistant a question about your company's internal documentation or your application's codebase, only to receive a confident, but completely incorrect answer? This is one of the biggest limitations of Large Language Models (LLMs). While models like GPT, Claude, and Gemini are incredibly capable, they only know what they were trained on and cannot magically access your latest documentation, databases, or private knowledge. As a result, they may "hallucinate" producing answers that sound convincing but are inaccurate or outdated. This is where Retrieval-Augmented Generation (RAG) comes in. Rather than relying solely on the model's built-in knowledge, RAG first retrieves relevant information from trusted sources such as PDFs, databases,