#Prompt Engineering
26 related articles
Prompting Claude Opus 5.5: How to Work More Effectively with the New Model
How should developers adjust their prompting strategies for Claude Opus 5.5? This article covers practical tips, the shift from imperative to intent-based prompting, and community debates.
TutorialsClaude Code Hooks Explained: A Safety Net for When Rules Fail
Deep dive into Claude Code Hooks: why CLAUDE.md rules fail, and how PreCommand blocking, PostCommand reminders, and Stop Hooks form a reliable AI behavior constraint system.
TutorialsClaude Code Hooks Explained: A Safety Net for When Rules Fail
Deep dive into Claude Code Hooks: why CLAUDE.md rules fail and how PreCommand, PostCommand, and Stop Hooks provide a reliable safety net through on-demand context injection.
TutorialsCore Methodology of Prompt Engineering: Principles, Techniques, and Complete Practical Guide
Deep analysis of Prompt Engineering core methodology: from LLM principles to the three key principles of specific, rich, and unambiguous prompts, plus programming advantages in the AI era.
TutorialsEngineering Practices That Boosted Claude Code Skill Activation Rate from 25% to 90%
How Hooks+Skills+Commands+Agents work together in Claude Code to boost AI skill activation from 25% to 90%, with core config files and deployment guide.
TutorialsAI Keeps Going Off-Track? Use Code to Compile Your Materials into Task Context
AI answers always off-track? The problem isn't the model — it's your input. Learn how context compilation scripts transform scattered materials into structured task context, boosting AI output quality from 2 to 90 points.
TutorialsIntroduction to Prompt Engineering: The Real Reason AI Can't Understand You
Why does AI always give irrelevant answers? This article explains prompt engineering fundamentals from the probabilistic prediction principles of LLMs, teaching you how to communicate effectively with AI.
Expert OpinionsThe Best Programming Language of 2026 Is English
As AI coding tools take center stage, natural language skills are replacing hand-written code as the core developer competency. Here's why English is now the most important programming language.
TutorialsOpenCode + LLM Automated Environment Patching: A Complete JS Reverse Engineering Workflow
Learn how to use OpenCode with DeepSeek LLM and System Prompts to automate JS reverse engineering environment patching for Taobao, Xiaohongshu, and more.
TutorialsAI LLMs Auto-Patching Browser Environments: A Practical Guide to 10x JS Reverse Engineering Efficiency
Learn how to use AI LLMs like DeepSeek with System Prompts to auto-patch JS browser environments for reverse engineering, covering major platforms with 10x efficiency gains.
Deep DivesHarness Engineering Explained: Core Methodology and Implementation Guide for Industrializing AI Agents
Deep dive into Harness Engineering methodology: Agent=Model+Harness formula, the Prompt→Context→Harness evolution path, and a developer implementation guide.
TutorialsHarness Engineering Explained: A Guide to Building a Four-Step Closed-Loop AI Agent Work System
A deep dive into the Harness Engineering four-step closed-loop principle (Goal, Action, Verification, Memory), clarifying its relationship with Prompt Engineering, Context Engineering, and MCP.
TutorialsHarness Engineering in Practice: A Three-Layer Architecture for Taming AI Agent Development
A detailed guide to Harness Engineering's three-layer architecture for controlling AI Agent code generation quality, covering the Information, Constraint, and Automation layers with practical setup and pitfall avoidance tips.
TutorialsAI Agent Development in Practice: 5 Evolutionary Stages from API Calls to Multi-Agent Collaboration
A complete guide to AI Agent development evolution: from API First principles to multi-Agent collaboration, covering prompt optimization, context engineering, and memory mechanisms to build reliable enterprise Agent systems.
Deep DivesHarness Engineering: The Core Engineering Methodology for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: its definition, six core components, and production practices. Learn why Prompt and Context Engineering aren't enough for production-grade AI Agent systems.
TutorialsAI Agent Prompt Engineering: A Practical Guide to the Three-Layer Architecture
A practical guide to AI Agent prompt engineering using a three-layer architecture: System Layer, Input Layer, and Action Layer — with n8n examples.
Deep DivesFive Core Concepts of AI Programming: A Complete Guide from Prompt to Harness Engineering
A clear breakdown of five core AI programming concepts — Prompt Engineering, Context Engineering, Agent, Skill, and Harness Engineering — with real-world use cases and advice for indie developers.
TutorialsAndrew Ng's 2026 New Course: 4 Core Techniques to Go from AI Prompting Beginner to Expert
Andrew Ng's 2026 AI prompting course: master 4 core principles from context-giving and deep thinking to overcoming sycophancy and iterative workflows.
Deep DivesTwo Root Causes of Agent Context Rot: Distraction vs. Poisoning Explained
AI Agent getting dumber over time? Context Rot has two root causes: Distraction (attention dilution) and Poisoning (causal chain contamination). Learn symptoms, mechanisms, and remediation strategies.
Deep DivesContext Engineering Explained: The AI Paradigm Shift from Prompt to Context Engineering
Deep dive into Context Engineering: its core principles and practices. From Prompt Engineering to context design, orchestration, and optimization—exploring how Karpathy's new AI paradigm reshapes LLM app development and AI Agent construction.