Articles

The article systematically explains how to communicate effectively with Large Language Models, progressing from the basic “Spark Mode” to the rigorous “Engineering Mode.” The core concept is to treat LLMs as “probability-driven completion engines” rather than intuitive experts. The author proposes five unconventional mental models to help readers understand the working mechanism of LLMs and introduces the “Verifiable Specification (SPECS-V)” framework, which constructs high-quality prompts by clarifying scope, audience, evidence, constraints, steps, and acceptance criteria. The article also provides practical tools and methods such as “Task Card” templates, MVP Prompts, iterative loops, and covers context injection, verifiability, and common pitfalls. Finally, the article demonstrates how to transform vague questions into expert-level answers and summarizes the key points, aiming to help users significantly improve LLM interaction efficiency and output quality.

This article details recent OpenAI updates to ChatGPT, including the return of the model selector based on user feedback, and a significant increase in the Thinking Mode quota for Plus users. It focuses on the GPT-5 model, highlighting its role as the default flagship model and its advanced deep reasoning capabilities, which enable automatic switching between Chat and Thinking modes based on task complexity. The article also outlines the availability and usage guidelines for GPT-5 and its various modes (Fast, Thinking, Pro) across different subscription levels. It anticipates that GPT-5 will be optimized to be more personable, concise, and personalized. Furthermore, the article provides 10 practical GPT-5 Prompt Engineering tips, covering deep reasoning, self-review, role constraints, and multimodal integration to help users maximize GPT-5's potential for creation and problem-solving. Finally, it briefly introduces Claude Sonnet 4's advancements in context length, underscoring the competition among Large Language Models in this area.