AI Applications
AI explainers, practical tool tutorials, and automation — from how large language models work to ChatGPT, Claude, and productivity apps, helping you understand and harness AI to boost work and life efficiency.
4 articles
Prompt Engineering: Structured Prompts & Dialogue Design
A hands-on guide to structured prompt engineering and multi-turn dialogue design. Learn the six core prompt modules, intent routing, context management, and advanced techniques like few-shot and JSON-schema outputs to make LLM responses stable and controllable.
The Real Moat for Developers in the AI Coding Era
As Coding Agents and Vibe Coding mature, programming language choice is no longer a constraint. The two edges that AI cannot easily replace are system integration and deep business understanding.
What is RAG and What Role Does Embedding Play?
Retrieval-Augmented Generation (RAG) has become the standard architecture for enterprise LLM applications, solving hallucination, knowledge cutoff, and private-data issues by letting models 'look up' facts before answering. Embedding is the soul of RAG—converting text into semantic vectors so similar meanings can be matched in vector space. This article explains the RAG pipeline, how Embeddings work, and how they empower production-grade intelligent systems.
How to Use AI for Stock Trading? A Complete Roadmap from Concept to Live Trading
AI-driven stock trading is no longer science fiction—it is already happening. This article breaks down the practical applications of AI in stock trading, from data collection, feature engineering, and model training to live deployment, helping you avoid the most common pitfalls.