RACEF Prompt Word Framework
Master the RACEF prompt word framework to improve the interaction with AI and achieve efficient communication.
Core content:
1. RACEF framework definition and five key components
2. Typical scenarios applicable to the framework and compatibility with mainstream AI models
3. Framework example demonstration and analysis of advantages and disadvantages
The Prompt Framework is a structured approach for building clearer, more effective prompts for better interaction with language models like me.
For example, it can improve the quality of prompt words, help us clarify the goal, context and expected output, avoid vague or ambiguous expressions, and obtain more accurate answers. It can help us save time. By reusing structured prompt templates, you can build high-quality prompts faster without having to start from scratch every time. It is more suitable for complex tasks. For complex tasks that require multi-step reasoning, role-playing, formatted output, etc., the prompt word framework can provide a clear structure to help the model better understand and execute.
This article is the first in a series of prompt word frameworks. The prompt word framework I’m going to introduce to you today is RACEF.
1. What is the RACEF framework?
RACEF is a prompt word engineering framework for improving AI interaction effects. It consists of five key components:
Rephrase : Rephrase the problem to make it clearer and more precise. Append : Add details or constraints to guide the AI to output more targeted content. Contextualize : Provide background information to make AI's answers more relevant to actual needs. Examples : Add examples to clarify the expected output format or content. Follow-Up : Encourage the AI to further improve the results by asking questions or making corrections.⠀
This framework emphasizes the combination of structure and flexibility, is applicable to a variety of complex tasks, and improves the clarity, depth, and adaptability of prompt words.
2. Application scenarios of the RACEF framework
The RACEF framework is applicable to the following typical scenarios:
Market research and strategic analysis Educational curriculum development Product Development and User Research Corporate training and employee benefits design Digital Marketing and Content Creation Policy making and social issues analysis AI system design and feedback analysis
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3. The most suitable model for RACEF framework
The RACEF framework is highly compatible with the following mainstream generative AI models:
| Model Name | Features |
|---|---|
| OpenAI GPT-4 | |
| Google Gemini | |
| Anthropic Claude | |
| Juuzt AI Proprietary Model | |
| Meta Llama 2 |
4. RACEF Framework Example
Example 1: Market research report
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Example 2: Educational curriculum development
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5. Advantages and Disadvantages of the RACEF Framework
advantage
Highly flexible : suitable for a variety of tasks such as creative, technical, analytical, etc. Clear structure : Five steps to help build high-quality prompt words Support iterative optimization : achieve continuous improvement through Follow-Up
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shortcoming
The learning curve is steep : a certain foundation in prompt word engineering is required Time-consuming : Constructing the complete cue word may be tedious for simple tasks
