RAG with LangChain4j — Part 1: What is RAG? | What Happens When an LLM Doesn't Know?

🚀 RAG with LangChain4j — Part 1: Introduction & Problem Statement What happens when you ask an LLM a question about information that it doesn't know? In this video, we take a step back from coding and understand RAG (Retrieval-Augmented Generation) and why it is needed when working with Large Language Models. Instead of directly jumping into the implementation, we'll first understand the problem through a practical demonstration using a Spring Boot application and a sample PDF. 📌 In this video, we'll cover: 🔹 What is RAG (Retrieval-Augmented Generation)? 🔹 Why do we need RAG? 🔹 How does a traditional LLM respond when it doesn't have the required information? 🔹 Can an LLM automatically access information from an uploaded PDF? 🔹 Live demonstration of the problem using a Spring Boot application 🔹 How RAG provides external knowledge to an LLM 🔹 The basic RAG flow: Retrieve → Augment → Generate 🔹 What we'll implement in the next part 🤔 The problem Suppose we have a PDF containing information that isn't part of the LLM's knowledge. We ask: "What is mentioned in this PDF?" Simply uploading the PDF doesn't automatically mean the LLM can use its contents. This is where RAG comes in. RAG allows an application to retrieve relevant information from an external knowledge source and provide that context to the LLM before generating the answer. 🔜 What's next? In Part 2, we'll implement the complete RAG solution using LangChain4j + Mistral AI, including document processing, embeddings, vector storage, retrieval and validation using Postman.    • RAG with LangChain4j — Part 2: Complete RA...   GIT REPO: https://github.com/aditya8642/langcha... #RAG #LangChain4j #retrievalaugmentedgeneration #generativeai #java #springboot #mistralai #ai #llm #genai #javadeveloper #artificialintelligence #RAGTutorial #vectordatabases #aiengineering