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Prompt Engineering: 10 Techniques That Actually Work
Master prompt engineering with Zero-shot, Few-shot, Chain-of-Thought, and ReAct techniques. Practical examples and strategies for GPT-4 and Claude models.
Prompt Engineering Mastery
Master the art and science of prompt engineering - from basic techniques to advanced strategies for production AI systems.
10 Articles in This Series · 创建于 2026-02-06
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Prompt Injection Attack & Defense Complete Guide [2026] - Essential AI Security Knowledge
Protect AI apps from prompt injection attacks. Learn direct/indirect injection types, jailbreak techniques, and defense strategies with code examples.
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Complete Guide to Context Engineering: The Evolution from Prompt Engineering
A deep dive into Context Engineering. Explore why precise context management is more important than prompts in 2026. Learn core strategies: Selection, Retrieval, Compression, and Persistence.
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Context Engineering Practical Guide: How to Provide the Perfect Context for AI
Master the practical strategies of Context Engineering. Learn how to build 'Task Dossiers,' leverage CLAUDE.md for long-term memory management, and optimize the token window to improve AI code output quality.
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Prompt Injection Defense: Building a Robust LLM Firewall
An in-depth analysis of the principles of Prompt Injection attacks, providing engineered defense methods. From data sanitization to structured Prompt isolation, learn how to build a simple LLM firewall middleware to protect the security of AI applications.
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What is LLM Hallucination? How to Detect & Prevent It
LLM hallucinations occur when AI generates plausible but false information. Learn detection methods, RAG strategies, and prompt techniques to build reliable AI apps.
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Context Window and Token Complete Guide: LLM Tokenization, Counting Methods, and Cost Optimization
Deep dive into Token and Context Window concepts in large language models, including BPE, WordPiece tokenization algorithms, model context window comparison, and practical methods for token counting and cost optimization.
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LLM Function Calling: Connect AI to Real-World Tools
Enable LLMs to call external APIs and tools. Comprehensive guide covers OpenAI function calling, JSON Schema, parallel calls, and the new MCP protocol with practical Python code examples.
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Advanced Cursor: Building an Efficient Team-Level Prompt Template Library
For Cursor users, explore how to accumulate and share efficient System Prompts and context rules within a team. This article details the advanced usage of `.cursorrules` to help you build standardized AI-assisted programming guidelines.
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Advanced Usage of Cursor and Trae: Building System-Level Prompts and Context Workflows for AI-Assisted Programming
Say goodbye to simple 'write some code for me' requests and dive deep into the advanced usage of AI IDEs like Cursor and Trae. This article details Context Engineering, system-level Prompt writing paradigms, and how to significantly improve the success rate of code refactoring and test generation through automated workflows.