Discuss AI and architecture; a ten-year veteran of major tech companies now transitioning to AI and continuing the journey.
This article analyzes the fundamental shortcomings of large language models for enterprise use, explains how Retrieval‑Augmented Generation (RAG) bridges those gaps through a detailed offline‑online workflow, and explores emerging trends that will shape the next generation of intelligent AI architectures.
This article introduces the second stage of a large‑model application series, detailing the value of Retrieval‑Augmented Generation (RAG), its architecture, and a step‑by‑step outline covering data cleaning, text chunking, vectorization, vector‑DB selection, recall strategies, reranking, and prompt construction.
This article outlines why most corporate IT systems remain AI‑inaccessible, proposes encapsulating functions, processes, and employee expertise as reusable "Skills", and details a three‑step method—identifying high‑frequency workflows, decomposing them, and packaging them as callable AI skills—plus the supporting architecture and ecosystem.
This article provides a practical selection guide for developers building AI agents in 2026, dissecting the design, core components, strengths, and limitations of four major frameworks—LangChain, LlamaIndex, LangGraph, and AutoGen—while offering use‑case recommendations, code examples, and a decision‑tree to help choose the most suitable tool.
This article curates ten free, open‑source AI‑focused projects—from web‑crawlers and browser automation to audio transcription, video downloading, and persistent memory—explaining their core capabilities, typical use cases, and how they can be integrated into developer workflows to boost productivity.
This guide walks through creating a fully automated WeChat public‑account article publishing pipeline using large‑model prompt engineering, covering token retrieval, title generation, subtitle creation, hand‑drawn comic generation, content formatting, image handling, and final draft publishing with detailed code snippets.
This guide presents a comprehensive prompt engineering framework—including the CRIS four‑step template, RAG‑based prompt construction, and Agent‑oriented architectures—illustrated with practical examples and optimization tips for tasks such as code generation, data extraction, and customer support, helping developers achieve stable, accurate LLM results.
This article explains the principles and common scenarios of prompt injection attacks on LLMs and provides practical defense strategies—including rule reinforcement, input filtering, output verification, and advanced techniques—to protect AI systems from malicious manipulation.
