Why Prompt Engineering Remains a Core Competency in the AI Era

"AI is so powerful now, can't I just chat with it naturally? Do I still need to specifically learn 'Prompt Engineering'?"

This repository documents the system prompts of top-tier AI applications:

https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools

If you dig into the system prompts behind these AI applications, you will find that some are surprisingly long—reaching hundreds of thousands of tokens. True AI experts never stop refining their prompts because they know: Precise instructions are the only key to evolving AI from a "chatty machine" into a "super productivity tool."

Next, I plan to release a series of posts deconstructing the techniques for writing great prompts—how to command AI like a general leading an army.

Structuring: Giving AI a Clear Framework

To obtain stable and consistent output, it is recommended to break your prompt into four core components: Background, Task, Guidelines, and Constraints.

Place the most core task and the most critical persona/identity at the beginning. If there are absolute "deal-breakers" (e.g., "Do not use LaTeX" or "Must respond in Chinese"), do not worry about being repetitive—reiterate these vital requirements at the very end.

Be mindful that if a conversation becomes too long, the model may forget its initial settings. For instance, moely.ai prompts you to start a new chat after 20 turns; this is to "refresh the context" and prevent the dialogue from being "contaminated" by a lengthy history.

The attention mechanism of the Transformer architecture (the backbone of LLMs) makes the model most sensitive to information at the beginning and the end of the input.

The Core Principle: C.L.E.A.R.

This is a highly practical and reusable mental model for prompting.

• Concise
  Get straight to the point and cut the fluff. Vague questions only yield vague answers.
• Logical
  Deconstruct complex requests into ordered steps. Resolve one core issue at a time. With clear logic, the model can complete tasks sequentially.
• Explicit
  Be clear about what you want and what you don't want. If the output style, tone, or format is important, state it explicitly.
• Adaptive
  Effective prompts are rarely written in one go. Treat AI as a collaborator that can be continuously calibrated, rather than a one-off tool.
• Reflective
  After each interaction, review which phrasing yielded the best results. By analyzing the AI's reasoning logic, you can build a continuous improvement loop—you can even ask the AI to summarize its final solution logic.

The more structured and logical your prompts are, the closer the AI's performance will get to a professional standard. In the long run, prompting is a reflection of your thinking ability—the capacity to break down complex needs and express them through structured language.