40 related articles

Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.

Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.

A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.

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.

A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.

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.

An in-depth look at an intelligent paper writing platform built on FastAPI + Vue 3, combining LLM, RAG, and multi-Agent collaboration for full-process automation—an excellent case study for AI developers.

A deep dive into an AI paper writing system built with FastAPI + Vue3, covering multi-agent collaboration, RAG, streaming output, and full academic workflow automation.
AstrBot: A Deep Dive into the Multi-Pl…
AstrBot is an open-source AI Agent framework supporting WeChat, QQ, Telegram and more, with multi-LLM compatibility and plugin extensibility. Full technical breakdown inside.
Structured Information Extraction with…
Using Qwen 2.5 7B quantized locally to extract 60+ fields from insurance/financial contracts? Learn why it struggles and how task splitting, RAG, GBNF, and smarter chunking can fix it.
Building an Offline RAG App from Scrat…
Learn how to build a fully offline RAG app using Ollama, ChromaDB, and Flask for local PDF Q&A. Covers chunking, vectorization, retrieval, and hallucination prevention.

A step-by-step breakdown of building a local RAG app: Ollama local models + ChromaDB vector database + Flask, enabling PDF document Q&A, fully offline operation, and zero data leakage. Perfect for developers new to RAG.

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.

An in-depth analysis of the "any Agent as an orchestrator" design philosophy, exploring the technical implementation of multi-Agent collaboration, context management, and workflow automation.

Frontend hiring now treats AI capabilities as a core assessment, covering RAG knowledge bases, AI Agent development, and LangChain.js engineering. Learn how LangChain.js + Nuxt.js helps frontend developers build memory- and retrieval-capable AI full-stack apps.

Step-by-step guide to building a complete RAG pipeline with Ollama + LangChain + FAISS + Qwen 1.5B. Run document retrieval and intelligent Q&A locally without a GPU.

A comprehensive guide to building enterprise knowledge bases with RAG, covering vector database selection, text chunking, Embedding models, multi-strategy retrieval, re-ranking, and Agent integration for high-accuracy AI Q&A systems.

A complete learning path for AI Agent development from scratch, covering core theory, ReAct paradigm, multi-agent collaboration, Prompt optimization, and hands-on projects across four stages.

A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.

A practical guide for Java developers to build AI apps without switching languages — covering LLM APIs, prompt engineering, RAG, Spring AI, and Langchain4j.