10 Levels of ChatGPT Prompting: Beginner to Award Winning

The video script outlines a 10-level framework for crafting effective prompts for AI chatbots, starting from basic requests to advanced techniques that won Singapore's prompt engineering competition. The levels include basic formatting, clear focus, providing examples, self-reflection, system prompts, personas, chain of thought, self-prompting, and the CO-STAR framework. Each level builds on the previous one, enhancing the clarity, specificity, and effectiveness of the prompts. The CO-STAR framework, which stands for Context, Objective, Style, Tone, Audience, and Response, is highlighted as the most effective method for structuring prompts to guide AI in generating responses that meet specific user needs.

Takeaways

• 📝 **Level 1 - Basic Request**: Start by simply telling the AI what you want, without much thought behind it.
• 📐 **Level 2 - Basic Formatting**: Use simple formatting like dashes to separate sections, and be polite and positive in your requests.
• 🎯 **Level 3 - Focused Requests**: Be clear and focused on what you want from the AI, specifying details to avoid ambiguity.
• 📚 **Level 4 - Give Examples**: Provide example inputs and outputs to guide the AI towards the desired response format.
• 🤔 **Level 5 - Self-Reflection**: Ask the AI if it missed anything, leveraging its strength in evaluation over generation.
• 📜 **Level 6 - System Prompt**: Use a special set of instructions to guide the AI in how to answer, providing context about yourself and your preferences.
• 🧑‍💼 **Level 7 - Use Personas**: Instruct the AI to adopt a persona or expertise relevant to the question, which can improve response accuracy.
• 🤓 **Level 8 - Chain of Thought**: Ask the AI to explain its thought process step by step, which can lead to better problem-solving.
• 📝 **Level 9 - Self-Prompting**: Let the AI create its own prompt to get the answer, as it may be better at prompting itself than humans are.
• 🌟 **Level 10 - COSTAR Framework**: Organize your prompt using the COSTAR framework (Context, Objective, Style, Tone, Audience, Response) for a structured and effective request.
• 🚀 **Advanced Techniques**: The video also discusses advanced techniques like appealing to emotions and using riddles to improve the AI's responses.

Q & A

What are the 10 levels of ChatGPT prompting as described in the video?

The 10 levels of ChatGPT prompting are: 1) Basic telling, 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 the prompt improve the results from ChatGPT?

Formatting the prompt, such as using dashes to separate sections, can greatly help ChatGPT understand the different parts of the prompt, leading to better responses, especially as the prompts become more complex.

What is the impact of politeness in prompts on the performance of large language models?

Research indicates that being polite in prompts can help improve the accuracy of large language models, as it seems to align with how these models process instructions.

Why is it suggested to tell ChatGPT to do something rather than not do something?

Large language models perform better when given affirmative instructions instead of negative ones. This is possibly because our brains are better at processing what to do rather than what not to do.

How can appealing to intense emotions improve the responses of large language models?

Appealing to intense emotions can improve the responses of large language models by making the task feel more important, which may lead to more careful processing of the request.

What is the purpose of being clear and focused in level three of the prompting?

Being clear and focused helps the chatbot understand exactly what information or action is desired, leading to more precise and relevant responses.

How does providing examples help in advanced prompting techniques?

Providing examples gives the chatbot a template to follow, which can improve the quality of the response by aligning it more closely with the desired output.

What is the purpose of self-reflection in level five of the prompting?

Self-reflection allows the chatbot to evaluate its own response, playing to its strengths in evaluation over generation, and can help identify any missing information.

How does using a system prompt in level six improve responses?

A system prompt provides a special set of instructions that guide ChatGPT to answer in the desired manner, often by giving it more context about the user and their preferences.

What is the benefit of using personas in level seven?

Using personas can improve the accuracy of responses by 6 to 20%, as it allows the chatbot to assume the role of an expert in the subject being discussed.

How does the chain of thought in level eight help with complex problems?

Asking the chatbot to explain its thought process step by step can lead to better outcomes, as it forces the model to break down the problem and address it methodically.

What is the concept of self-prompting in level nine?

Self-prompting involves asking the chatbot to create its own prompt to find the answer, leveraging the model's ability to generate prompts better than humans in some cases.

Can you explain the Co-star framework in level ten?

The Co-star framework organizes the prompt into specific parts: Context, Objective, Style, Tone, Audience, and Response. It guides ChatGPT to provide responses tailored to the user's exact requirements.

Outlines

00:00

🔍 Prompt Engineering Levels

The paragraph introduces a 10-level system for optimizing prompts to get the best results from chatbots. It begins with basic, mindless instructions and advances to complex techniques that won a prompt engineering competition in Singapore. The speaker shares insights on how to improve responses, such as basic formatting, being polite, focusing requests, giving examples, self-reflection, and using system prompts effectively.

05:01

🧐 Advanced Prompting Techniques

This paragraph delves into more advanced techniques for interacting with chatbots. It discusses the use of personas to improve accuracy, the 'Chain of Thought' method for complex problems, self-prompting where the chatbot creates its own prompts, and the 'CO-STAR' framework for structuring prompts. The CO-STAR framework is highlighted as a method used by the speaker to win a competition and is broken down into Context, Objective, Style, Tone, Audience, and Response, providing a structured approach to prompt engineering.

Keywords

💡Prompt Engineering

Prompt engineering refers to the art and science of formulating questions or instructions in a way that guides an AI system, like ChatGPT, to generate the most useful, accurate, or desired response. In the video, it is the central theme, with the presenter discussing various levels of sophistication in crafting prompts to achieve better outcomes from AI.

💡Large Language Models (LLMs)

Large Language Models are AI systems that have been trained on vast amounts of text data and can generate human-like responses to text-based queries. They are characterized by their ability to understand and produce language at scale. In the context of the video, the presenter is discussing how to effectively interact with and extract value from such models.

💡System Prompt

A system prompt is a specific type of instruction given to an AI that outlines how it should behave or respond. It often includes details about the user's preferences, context, and desired outcomes. In the video, the presenter mentions using system prompts to guide the AI towards more personalized and accurate responses.

💡CO-STAR Framework

The CO-STAR Framework is a structured approach to creating prompts, where each letter stands for a different aspect of the prompt: Context, Objective, Style, Tone, Audience, and Response. It is presented in the video as an effective method for organizing and enhancing the quality of prompts given to AI.

💡Chain of Thought

Chain of Thought is a prompting technique where the AI is asked to articulate its reasoning process step by step. This method is shown in the video to be effective for solving complex problems by encouraging the AI to break down the problem into manageable parts.

💡Self-Prompting

Self-prompting is a technique where the AI is instructed to generate its own prompt based on the information provided. It leverages the AI's ability to understand and generate prompts, potentially leading to more accurate or detailed responses. The video demonstrates how this technique can be used to solve riddles or complex queries.

💡Personas

In the context of AI interaction, personas are roles or characters that the AI is asked to assume to improve the relevance and accuracy of its responses. The video discusses how adopting personas can lead to a significant increase in the quality of the AI's output.

💡Self-Reflection

Self-reflection, when used in the context of AI, involves the AI reviewing its own output to identify any missing or incorrect information. The video script mentions using self-reflection as a technique to enhance the thoroughness of the AI's responses.

💡Formatting

Formatting in the context of prompt engineering refers to the structuring of the prompt to improve clarity and comprehension for the AI. Simple formatting techniques like using dashes or being clear and focused can greatly enhance the AI's ability to understand and respond to the prompt effectively, as discussed in the video.

💡Emotional Appeal

Emotional appeal is the use of emotional language in prompts to elicit a desired response from the AI. The video suggests that appealing to intense emotions can sometimes improve the responses of large language models, although this technique is used sparingly.

💡Focus Requests

Focus requests are a type of prompt that is clear and specific about what information is required from the AI. By avoiding ambiguity and specifying exactly what is needed, as shown in the video, the quality of the AI's response can be significantly improved.

Highlights

10 levels of ChatGPT prompting have been identified, ranging from beginner to award-winning techniques.

Level one involves basic requests without much thought, sometimes yielding good results, sometimes not.

Level two introduces basic formatting, such as dashes, to improve ChatGPT's understanding of prompt sections.

Politeness in prompts can enhance the accuracy of large language models.

Using imperatives rather than negatives can lead to better performance in language models.

Appealing to intense emotions can improve the responses of large language models.

Level three focuses on clear and focused requests to the chatbot for better quality responses.

Level four involves giving examples to guide ChatGPT, known as 'fuse shot' technique.

Self-reflection, or asking ChatGPT if it missed anything, leverages its strength in evaluation over generation.

The system prompt (level six) guides ChatGPT with specific instructions on how to answer.

Using personas can improve the accuracy of responses by 6 to 20%.

Level eight's 'Chain of Thought' involves asking ChatGPT to explain its thought process for complex problems.

Self-prompting (level nine) suggests that large language models are better at prompting themselves.

The CO-STAR framework (level ten) organizes prompts into context, objective, style, tone, audience, and response format.

CO-STAR framework was used to win Singapore's ChatGPT prompt engineering competition.

The framework ensures ChatGPT provides responses exactly as desired by the user.

Using CO-STAR improves the quality and relevance of the responses for specific tasks.

The video provides practical examples of how to apply each level of prompting for better results.

Further videos on advanced AI usage are planned for release.