格 千格 KiloCell

Prompt Skills Quick Check 2025

Mastering the tip project core, releasing the maximum potential of the AI model

Prompt Engineering?

Prompt Engineering (Phrasing) is a technique that leads to higher quality and better alignment of the output of the Big Language Model (LLM) by optimizing input instructions. Yes. In 2025, as the ability to deduce models increases, Prompt It's not just simple commands, but it's evolved into mechanisms that include logical frameworks, context guidance and self-correction. "Natural language programming" 。

Core logic: AI The performance depends not only on the model itself, but also on how you define the mission. With a reasonable hint technique, you can upgrade the performance of a normal model to an expert level.
♪ Core progression techniques ♪
COT Progressive reasoning Secondary

Let AI show the reasoning process, not just give the answer. Significant increase in the accuracy of logical and mathematical tasks.

Trigger word "Let's think one by one." "Please explain your reasoning."
🔄React Frame Advanced

Combining reasoning with action, simulates how humans solve problems. is the core model of AI Agent.

Structure Thought → Action → Observation → Thought → ...
Step-Back Step Back Secondary

Let AI think about the general principles before solving specific problems. Suits a complex professional advisory landscape.

Apply " Before answering how to optimize components, please summarize the rendering principles of React. "
🌳Tree-of-Thought Think Tree Advanced

At the same time, multiple reasoning paths are explored, the feasibility of each path is assessed and the ultimate choice is the optimal solution.

Skills AI is required to generate 3 different programmes and evaluate each other.
📋CRISPE Phranometric Framework

This is the most recommended systematic hint construction framework for 2025:

C

Capacity (identity)

Sets the role of AI. For example, "You're a senior architect with 20 years of experience. ”

R

ReQuest (task)

Identify the specific tasks requested. For example: “Please evaluate the following codes and give a re-engineering scheme. ”

I

Input (Input)

Provides the necessary background data or context. For example: "This is code content: [coding block]"

S

Specification (Constraint)

Defines the output limit. For example: "Do not exceed 500 words, use Markdown format. ”

P

Prizes (reward mechanisms)

Optional. The focus of the model is increased by psychological inducement. For example, “It's very important for my career. ”

E

Examples (Example)

Provides 1-2 success stories (Few-Shot). This is the most effective way to improve coherence.

Depth resolution: Why is your AI always indifferent? 2025 Guide to War-Screeching.

Many users often complain about AI when using ChatGPT or Claude It's "bullshit" or "fantasia." This is not usually a problem with models, but rather a lack of design of the hints. At 2025. In the year, you will be able to leave this in vain with the following operational skills:

1. A farewell to vagueness and structured outputs of hugs

Error demonstration: "Separate this article for me. ”
Professional model: “Please analyse the emotional trends of the following articles. Please return in JSON format, containing the content (positive/negative/neutral) Confidence (0-1), keywords (array) four fields. ”
The structured output not only makes the results easier to read, but also forces AI to perform deeper logical classifications.

2. Addressing specific issues: the battlefield of the narrative engineering

  • Technical remodelling: Use Step-Back Skills. First let AI summarize code design mode, then re-engineer it based on mode. This way, I can avoid AI. Gives a trash code that can run but is not structured.
  • Complex decision-making: Use Tree-of-Thought 。 Request AI The same programme was debated in a simulation of the roles of “finance director”, “technology specialist” and “product manager”, which resulted in a comprehensive assessment.
  • Automated processes: Use ReAct Mode. Provides AI with available API documents that guide it to accomplish multi-step tasks autonomously through the Think-Action-Watch cycle.

3. Scientificity of psychological insinuations and incentive mechanisms

Various studies have shown that the words "it's important to me" and "I'll give you 200" are added to the hint. Psychological inducements such as “dollar tips” or “breath and act one step at a time” do inspire models to invest more in the reasoning phase. It sounds funny, but... In the LLM ' s attention mechanisms, these strong emotional terms add weight to the relevant logical path.

APE (Auto-Tip Optimization): Let AI optimize AI

If you're not sure how to write perfect Prompt, you can try this technique: send your original command to AI, and asked: “How can I improve this hint in order to allow you to produce the best possible results? Please give me three improvements and explain why. ”

The KiloCell remind: Prompt Engineering is a practical science. With Models (e. g. o3, Claude) 3.5) Rapid iterative and evolving techniques. Keep the spirit of the experiment and keep fine-tuning, you can become a real AI master.