10 Levels of ChatGPT Prompting: Beginner to Award Winning
TLDRThis video script outlines a 10-level framework for crafting effective prompts to enhance the performance of AI language models like ChatGPT. Starting from basic requests to advanced techniques, the levels include clear and focused requests, providing examples, self-reflection, system prompts, personas, chain of thought, self-prompting, and the CO-STAR framework. The CO-STAR framework, which stands for Context, Objective, Style, Tone, Audience, and Response, is highlighted as an effective method for structuring prompts to receive desired responses. The script emphasizes the importance of politeness, specificity, and leveraging AI's strengths in evaluation over generation for improved outcomes.
Takeaways
- 📝 Level 1 is about stating your request directly to Chat GPT without much thought to the phrasing.
- 📐 Level 2 emphasizes basic formatting, such as using dashes to separate sections, which can improve understanding and response quality.
- 🙏 Level 3 is about being polite and using positive language, which can enhance the accuracy of large language models.
- 🎯 Level 4 focuses on being clear and focused in your requests, specifying exactly what information you want and in what format.
- 📚 Level 5 involves giving examples to guide Chat GPT, which can be particularly effective for more complex tasks.
- 🤔 Level 6 is about self-reflection, asking Chat GPT if it missed anything, which leverages its strength in evaluation over generation.
- 📜 Level 7 introduces the concept of using personas to guide Chat GPT's responses, which can significantly improve accuracy.
- 🤓 Level 8 encourages a 'Chain of Thought' approach, asking Chat GPT to explain its reasoning process step by step.
- 💡 Level 9 is about self-prompting, where Chat GPT is asked to create its own prompt to better answer the question.
- 🌟 Level 10 introduces the CO-STAR framework, which organizes prompts into Context, Objective, Style, Tone, Audience, and Response format for optimal results.
- 📈 The CO-STAR framework was used to win Singapore's GPT-4 Prompt Engineering competition, showcasing its effectiveness.
Q & A
What are the 10 levels of ChatGPT prompting mentioned in the video?
-The 10 levels of ChatGPT prompting are: 1) Basic request, 2) Basic formatting, 3) Focused requests, 4) Giving examples, 5) Self-reflection, 6) System prompt, 7) Use personas, 8) Chain of thought, 9) Self-prompting, and 10) Co-star framework.
How can formatting help in improving the responses from ChatGPT?
-Formatting, such as using dashes to separate sections of the prompt, can help ChatGPT understand the different parts of the request more clearly, leading to better responses, especially as the prompts become more complex.
What is the significance of being polite in your prompts to ChatGPT?
-Research indicates that being polite in prompts can help improve the accuracy of responses from large language models like ChatGPT. Politeness may also contribute to better interaction habits.
How does appealing to intense emotions in prompts affect the responses from large language models?
-Appealing to intense emotions can improve the responses of large language models. For instance, emphasizing the importance of the task or the personal significance of the outcome can lead to more accurate and focused responses.
What is the purpose of being clear and focused in level three of ChatGPT prompting?
-Level three focuses on clarity and specificity in the request to the chatbot. By being clear about what information is needed and how it should be presented, the quality of the response is significantly improved.
How does providing examples help in advanced prompting techniques?
-Providing examples of both the input and the desired output helps ChatGPT understand the expected format and content of the response, leading to more accurate and relevant answers.
What does the 'system prompt' involve and how is it used?
-The system prompt is a set of instructions that guides ChatGPT to answer in a specific way. It involves giving ChatGPT context about the user and their preferences, which can lead to more personalized and accurate responses.
How can using personas enhance the performance of large language models?
-Using personas, or instructing the model to act as an expert in a certain field, can improve the accuracy of responses by 6 to 20%. It helps the model to focus its knowledge and generate more relevant answers.
What is the 'Chain of Thought' technique and how does it help in solving complex problems?
-The 'Chain of Thought' technique involves asking ChatGPT to explain its thought process step by step. This helps the model to handle tough problems by breaking them down into a logical sequence of steps.
What is the concept of 'self-prompting' and how does it benefit the interaction with ChatGPT?
-Self-prompting involves asking ChatGPT to create its own prompt to get the desired answer. It leverages the model's ability to generate prompts better than humans can, leading to more effective responses.
Can you explain the CO-STAR framework and its components?
-CO-STAR is a prompt organization framework consisting of: C for Context, where relevant background information is provided; O for Objective, with clear instructions on the task; S for Style, defining the writing style; T for Tone, setting the emotional tone of the response; A for Audience, identifying the target audience; and R for Response, specifying the format of the reply.
How does the CO-STAR framework improve the quality of responses from ChatGPT?
-The CO-STAR framework improves response quality by guiding ChatGPT through every aspect of the desired answer, from context and objective to style, tone, audience, and format, ensuring the response is tailored to the user's needs.
Outlines
🔍 Prompt Engineering Levels: From Basic to Expert
The video introduces 10 levels of prompt engineering, starting with basic commands and progressing to advanced techniques. The presenter shares insights from research and testing to optimize interactions with chatbots, like chat GPT. Level one involves straightforward requests to the chatbot, while level two emphasizes basic formatting to improve understanding. Level three focuses on clear and focused requests, and level four introduces the concept of providing examples for the chatbot to follow. Level five involves self-reflection, asking the chatbot if it missed anything, and level six discusses the use of system prompts to guide the chatbot's responses. The video also touches on the effectiveness of using personas in level seven, the chain of thought in level eight, self-prompting in level nine, and concludes with the CO-STAR framework in level ten, which organizes prompts effectively for the chatbot.
🤔 CO-STAR Framework: Structuring Prompts for Better AI Responses
The CO-STAR framework is a method for structuring prompts to guide AI, like chat GPT, to provide responses that meet specific expectations. The framework is broken down into five components: Context (C), where background information is provided; Objective (O), which clarifies the task for the AI; Style (S), dictating the writing style; Tone (T), setting the emotional tone of the response; and Response (R), specifying the desired format of the output. The video demonstrates how using CO-STAR can transform a basic, ineffective request into a compelling and targeted message, like a Facebook post for a magic carpet business. The presenter also mentions upcoming videos on leveraging AI effectively.
Mindmap
Keywords
💡Prompt Engineering
💡Level One Prompt
💡Basic Formatting
💡Focus Requests
💡Examples
💡Self-Reflection
💡System Prompt
💡Personas
💡Chain of Thought
💡Self-Prompting
💡CO-STAR Framework
Highlights
10 levels of ChatGPT prompting have been identified, ranging from beginner to expert techniques.
Level one involves basic, direct requests to ChatGPT without much thought behind them.
Level two emphasizes basic formatting, such as using dashes to separate sections, which can improve understanding.
Politeness and avoiding negatives in prompts can enhance the accuracy of large language models.
Appealing to intense emotions can improve the responses of large language models, as demonstrated in level three.
Level four suggests giving examples to guide ChatGPT, which is an advanced prompting technique.
Self-reflection, as introduced in level five, allows ChatGPT to check its own responses for accuracy.
Level six introduces the system prompt, a set of instructions to guide ChatGPT's responses.
Using personas, as explained in level seven, can improve the accuracy of responses by 6 to 20%.
Level eight's 'Chain of Thought' method asks ChatGPT to explain its problem-solving process step by step.
Self-prompting, at level nine, involves ChatGPT creating its own prompts to find the answers.
The CO-STAR framework, presented at level ten, is a structured approach to organizing prompts for optimal results.
CO-STAR stands for Context, Objective, Style, Tone, Audience, and Response, each guiding a specific part of the prompt.
The framework has been used to win Singapore's ChatGPT prompt engineering competition.
Level three focuses on clarity and focus in requests to improve chatbot responses.
Providing specific examples and desired formats, as in level four, helps ChatGPT understand the expected output.
Level five's self-reflection technique leverages ChatGPT's strength in evaluation over generation.
The system prompt in level six tailors responses to the user's preferences and context.
Level seven's persona technique improves response accuracy by adopting specific expert roles.
Level eight's 'Chain of Thought' encourages a logical, step-by-step explanation from ChatGPT.
Self-prompting at level nine demonstrates ChatGPT's ability to generate its own prompts for complex tasks.
The CO-STAR framework synthesizes all learned techniques for a comprehensive prompting strategy at level ten.