#AI application development
3380 related articles
·3 min
A Practical Guide for Java Developers Transitioning to AI Application Development
Facing the AI wave, how can Java developers transition from traditional CRUD backend work to AI application development? This article breaks down the AI+ national strategy, China's AI catch-up logic, and offers a practical path.
Read more →
·3 min
LangChain4j Getting Started Guide: A Java Engineer's Fast Track to AI Application Development
LangChain4j is the AI application development framework built for Java engineers. Integrate DeepSeek, Qwen, and more into Spring Boot — no Python required.
Read more →
·3 min
LangChain Practical Guide: From RAG Applications to Intelligent Agent Development
A comprehensive guide to LangChain: core concepts, RAG applications, Agent development, version selection (0.3/1.0), and career opportunities for Java/Python developers entering LLM development.
Read more →
·3 min
Spring AI 2.0 Deep Dive: A Java Developer's Guide to LLM Application Development
A deep dive into Spring AI 2.0: provider-agnostic APIs, RAG with vector databases, and how Java developers can build LLM apps using the Spring ecosystem.
Read more →
·3 min
Getting Started with LangChain: The Core Framework for LLM Application Development and Its Three Key Components
LangChain is the leading open-source framework for LLM application development, supporting GPT-4, GLM, and other mainstream models. This article dives into its three core concepts: Components, Chains, and Agents.
Read more →
·3 min
Getting Started with AI Agent Development: A Complete Learning Path to Building Intelligent Applications from Scratch
AI Agents are reshaping software development with 42.8% market CAGR. Learn the difference between Agents and traditional AI, plus a complete LangChain-based curriculum to launch your career in intelligent agent development.
Read more →
·2 min
The Complete Guide to Custom Software Development: AI Large Model Applications & Full-Stack Development, All in One Place
15-year full-stack engineering team offering custom development for mini programs, apps, enterprise systems, RAG knowledge bases, and AI agents — no middlemen, no subcontracting, full one-on-one ownership.
Read more →
·3 min
AI Application Development Learning Roadmap: From Agents and RAG to Real-World Career Success
Want to break into AI application development? This guide covers the full learning path — from Agents and RAG to Prompt Engineering — helping you master LLM engineering skills and land the job.
Read more →
·6 min
LangChain 1.0 Comprehensive Guide: Three Core Frameworks and Hands-On AI Application Development
Deep dive into LangChain 1.0's architecture: LangChain framework, LangGraph multi-Agent orchestration, and LangSmith observability platform, with hands-on RAG and intelligent customer service projects.
Read more →
·6 min
Java Developer's Guide to AI Application Development: Building an Airline Intelligent Customer Service System
A practical guide for Java developers to build AI apps without switching to Python. Learn LangChain4j, RAG, Function Calling, and MCP through an airline customer service project.
Read more →
·4 min
Coze for Beginners: A Complete Guide to ByteDance's AI Application Development Platform
A complete guide to ByteDance's Coze platform: agents, AI apps, workflows, nodes, and plugins explained. Build AI applications with no coding required.
Read more →
·3 min
Full-Stack AI Development with Vercel: A Complete Guide to AI SDK, RAG, and Production-Ready Applications
Master full-stack AI development with Vercel: from LLM, RAG, and vector embeddings to AI SDK, AI Gateway, and v0 — build production-ready AI web apps end to end.
Read more →
·3 min
Programmer's Guide to the AI Era: Application Development Is a Real Opportunity for Ordinary Developers
Too hard to become an algorithm engineer? Too basic to just use AI tools? This guide breaks down the three levels of AI adoption for programmers, with a focus on Agent development and large model engineering — including salaries, timelines, and window risks.
Read more →
·3 min
Programmers Pivoting to AI: Opportunities in Application Development and Pitfalls to Avoid
How can ordinary programmers break into AI? This guide breaks down the gap between algorithm engineers and AI app developers, covering Agent development, model fine-tuning, salary trends, and the three hidden risks behind the current opportunity window.
Read more →
·4 min
Dify Local Deployment Tutorial: Build Your AI Application Development Platform from Scratch
Step-by-step guide to deploying Dify locally using BT Panel, covering VM setup, Ubuntu configuration, and Docker deployment for a private AI dev platform.
Read more →
·3 min
Java Developer's Guide to AI Application Development: From Spring AI to Intelligent Customer Service
A comprehensive guide for Java developers transitioning to AI application development, covering Spring AI, RAG, Function Calling, and a hands-on airline intelligent customer service project.
Read more →
·2 min
The 4-Stage Roadmap for AI Application Development: A Career Transition Guide from Beginner to Senior Engineer
A 4-stage roadmap for AI application development: from Python and RAG basics to Agent cluster architecture, covering the core skills needed for career growth.
Read more →
·3 min
AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning
Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.
Read more →
·3 min
Java Developer's Guide to AI Application Development: Career Path, Skills, and Hands-On Strategy
A practical guide for Java developers transitioning to AI app development. Includes a 45-day learning plan covering Spring AI, RAG, Agent skills, plus resume and interview strategies.
Read more →
·3 min
LangChain from Beginner to Agent Development: A Complete Learning Path for LLM Application Development
A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.
Read more →