#RAG
287 related articles
TutorialsGetting Started with RAG: What Is Retrieval-Augmented Generation and How Does It Solve LLM Hallucination?
RAG (Retrieval-Augmented Generation) is the core solution for LLM hallucination. Learn RAG concepts, how it works, three causes of hallucination, and the complete learning path from basics to Knowledge Graph RAG.
TutorialsFrom Traditional RAG to Agentic RAG: Core Principles and Implementation Guide
Deep dive into the technical differences between traditional RAG and Agentic RAG, covering offline/online pipeline principles, tool-based autonomous decision mechanisms, and a LangGraph-based Agentic RAG implementation via the ChatBox open-source project.
TutorialsEnterprise RAG Project in Practice: A Complete Guide from Principles to Deployment
Complete guide to enterprise RAG projects covering principles, LangChain implementation, data processing, retrieval optimization, evaluation, and cloud deployment for AI knowledge base applications.
Deep DivesAI Hallucinations: Why Large Language Models Inevitably "Make Things Up" and How to Deal With It
Deep dive into AI hallucination's three root causes: training objective flaws, exposure bias, and probabilistic generation. Covers classification and practical mitigation strategies including RAG.
TutorialsBuilding an AI Agent Mobile App with Zero Code: From Prompt to App Store — A Complete Walkthrough
Learn how to use VibeCodeApp to build a D2C e-commerce AI Agent mobile app with RAG capabilities using natural language prompts — from writing prompts to App Store deployment.
Deep DivesRAG System End-to-End Breakdown: From Vector Indexing to Production Optimization
A deep dive into the complete RAG pipeline — covering vector embeddings, document chunking, retrieval and reranking, plus three production optimization techniques for building accurate enterprise AI knowledge base applications.
TutorialsBuilding a Local Knowledge Graph RAG System with Dify + Neo4j: A Hands-On Guide
A hands-on guide to building a local knowledge graph RAG system using Dify, Neo4j, and Docker for multi-hop reasoning and secure local deployment.
TutorialsLangChain 1.0 Complete Guide: Architecture, Core Modules, and Learning Path
Deep dive into LangChain 1.0's three-layer architecture (LangChain, LangGraph, Deep Agents), core components like Models, Tools, and Memory, plus a complete learning path from semantic search to multi-agent collaboration.
TutorialsPractical Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Step-by-step guide to building a local RAG knowledge base using RAGFlow, Ollama, and LM Studio with Docker, covering Embedding model deployment and network troubleshooting for private AI Q&A.
TutorialsJava Developers Transitioning to AI Engineers: New Interview Trends and Learning Roadmap
Deep analysis of interview trends for Java developers transitioning to AI engineers, covering LLM integration, RAG, Spring AI framework practice, with a complete learning roadmap.
TutorialsAnythingLLM Installation & Configuration Guide: Building a Local Knowledge Base with API Integration
Complete guide to AnythingLLM local knowledge base setup: installation tips, Ollama model configuration, document vectorization, recall optimization, and API integration.
TutorialsAI Agent Learning Roadmap: A Complete Guide from LLM Fundamentals to Enterprise-Level Project Implementation
A systematic AI Agent learning roadmap covering Python setup, Prompt Engineering, RAG, LangChain, multi-Agent collaboration, with enterprise medical consultation system case study and phased learning plan.
TutorialsAI Large Language Model Learning Roadmap: Six Stages from Zero to Engineer
A systematic LLM engineer learning roadmap covering Transformer basics, prompt engineering, RAG, Agent development, API integration, fine-tuning, deployment, and project practice across six stages.
Deep DivesAI Agents 101: Core Concepts, Technical Architecture, and How They Work
A beginner's guide to AI Agents: understand core concepts, the perception-decision-action loop, LLM, tool calling, memory systems, and RAG architecture explained from scratch.
TutorialsComplete Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Complete guide to building a local AI knowledge base with Qwen3.5, RAGFlow, and Ollama, covering Docker deployment, Embedding model configuration, knowledge base creation, and RAG system setup.
TutorialsCan You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
Deep DivesCodeRAG Technical Deep Dive: Four Core Components That Help AI Truly Understand Your Codebase
Deep dive into CodeRAG's four core technologies: vector similarity search, file system tools, Code Knowledge Graph (CKG), and DeepWiki — how they work together to help AI coding assistants truly understand enterprise codebases and eliminate hallucinations.
Industry InsightsDeep Dive into Three Major LLM Career Paths: Requirements, Tech Stacks, and Career Prospects
Deep analysis of three core LLM roles—Application Engineer, Development Engineer, and Algorithm Engineer—covering technical requirements, salary thresholds, and career prospects including RAG, fine-tuning, and inference deployment.
TutorialsThe Complete Guide to Spring AI: A Full Learning Path for Java Engineers Building AI Applications
A comprehensive guide to Spring AI covering LLM integration, prompt engineering, RAG knowledge bases, and five AI Agent patterns, with three enterprise projects for Java engineers.
TutorialsCareer Progression in AI/LLM Application Development: Four Stages from Beginner to 40K Monthly Salary
A systematic four-stage career path for AI/LLM application development: from RAG and Agent fundamentals to architecture design, helping developers transition to AI roles targeting 40K+ monthly salary.