#LangChain4j tutorial
222 related articles
·7 min
A Java Developer's Guide to AI: Framework Selection and Practical Tutorial for LangChain4J
As LLM costs keep falling, how can Java developers seize the AI opportunity? This article explores LangChain4J's core capabilities, supported models and vector databases, and compares LangChain4J vs. Spring AI to help you build local knowledge bases and intelligent customer service systems.
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·4 min
LangChain4j in Practice: A Complete Tutorial for Building a RAG Medical Q&A System
A complete tutorial on building a RAG medical Q&A system with LangChain4j, covering Ollama local deployment, Redis vector DB, document vectorization, and Cursor AI-assisted development.
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·3 min
Practical Guide to Agent Development: The Complete Path from Zero to Commercial Deployment
A systematic guide to AI Agent development from beginner to deployment, covering task planning, tool calling, memory management, learning paths, and realistic commercial monetization considerations.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·2 min
A Beginner's Guide to Vibe Coding: Building Your Career Moat with AI Programming
A detailed guide to Vibe Coding with AI programming tools like Claude Code, Cursor, and Codex. Learn how to leverage AI-driven development to ship products independently and build lasting career value.
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·5 min
A Beginner's Guide to Prompt Engineering: From One-Off Instructions to a Systematic Methodology
A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy to Help You Fully Understand
Large models aren't search engines — they're more like super compressors. This article explains how LLMs compress data to learn semantic patterns, and explores the phenomenon of intelligent emergence.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·4 min
AI Agent Development in Practice: A Complete Path from Zero to Production
A systematic guide to AI Agent development covering core modules, framework selection, tool calling, data preparation, and production deployment to help developers build production-ready Agent applications.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy That Makes Everything Click
LLMs aren't search engines — they're more like super compressors. This article explains how large models compress corpora to learn semantic patterns, and explores the principles and limitations of emergent intelligence.
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·5 min
The Complete Guide to AI Agent Development: A Four-Stage Progressive Roadmap
A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.
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·3 min
claude-video: An Open-Source Tool That Teaches Claude to "See" Videos
claude-video is a trending open-source tool that enables Claude to analyze videos via frame extraction and audio transcription. Learn how it works and its use cases.
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·3 min
Getting Started with AI Agent Development: A Learning Path from Zero to Deployment
A systematic guide to the complete learning path for AI Agent development—covering prompt engineering, RAG knowledge bases, LangChain & LangGraph, fine-tuning, and multi-agent collaboration.
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·2 min
Getting Started with AI Agent Development: A Complete Guide from Zero to Real-World Projects
A beginner-friendly guide to AI Agent development, covering the full learning path from LLM basics, prompt engineering, and RAG to LangChain and multi-agent collaboration.
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·2 min
Getting Started with AI Agent Development: A Complete Guide from Zero to Deployment
A beginner-friendly guide to AI Agent development, covering the full learning path from LLM fundamentals, prompt engineering, and RAG to LangChain and multi-agent collaboration.
Read more →
·2 min
A Complete Guide to Learning AI Agents from Scratch: From Beginner to Practice
A complete guide to learning AI Agents: from large model fundamentals and core technologies to hands-on projects. Systematically outlines beginner methods and exposes crash-course marketing traps.
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·1 min
Becoming an AI Agent Engineer: A Detailed 4-Week Hands-On Learning Roadmap
Want to become an AI Agent engineer? This article breaks down a 4-week roadmap: from core agent architecture and ReAct, to multi-agent collaboration and real projects.
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·4 min
Spring AI 1.0 Practical Guide: A Java Developer's Fast Track to Building AI Applications
Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.
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·3 min
The Complete Beginner's Guide to Vibe Coding: Core Logic and Practical Learning Path
Vibe Coding is the new AI-era programming paradigm. Describe what you want in plain language; let AI generate the code. Learn the 3-stage path: mindset, quality, and real projects.
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