10 Levels of ChatGPT Prompting: Beginner to Award Winning

Summary

This video script offers a comprehensive guide to prompt engineering with AI, detailing 10 levels of expertise from basic to advanced techniques. It starts with simple instructions and evolves to complex strategies like using personas and the CO-STAR framework, which won Singapore's AI prompt engineering competition. The script emphasizes clear communication, formatting, and leveraging AI's strengths, such as self-reflection and chain of thought, to achieve optimal results.

Takeaways

• 🔍 The video discusses 10 levels of prompt engineering for AI, starting from basic to expert techniques that won a competition in Singapore.
• 📝 Level one is about straightforwardly telling the AI what you want without much thought, which can yield inconsistent results.
• 📐 Level two emphasizes the importance of basic formatting like dashes to separate sections, which can greatly help AI understand the prompt structure.
• 🌟 Level three focuses on being clear and focused in requests, specifying exactly what information is desired and in what format.
• 📚 Level four introduces the concept of giving examples to the AI, which can significantly improve the quality of responses by providing input-output pairs.
• 🤔 Level five suggests using self-reflection prompts to leverage the AI's strength in evaluation over generation, asking if it missed anything.
• 📜 Level six introduces the 'system prompt', a set of instructions guiding the AI to answer in a desired manner, tailored to the user's preferences and context.
• 🎭 Level seven talks about using personas to improve accuracy, with studies showing a 6-20% improvement when the AI acts as an expert in the subject.
• 💭 Level eight encourages the AI to explain its thought process, often by asking it to think step by step, which is beneficial for complex problems.
• 🤖 Level nine proposes self-prompting, where the AI is asked to create its own prompt to get the desired answer, leveraging its own prompting capabilities.
• 🌟 Level ten presents the 'CO-STAR' framework, which organizes prompts into context, objective, style, tone, audience, and response format for optimal results.

Q & A

What is the main topic of the video script?

The main topic of the video script is prompt engineering, specifically the techniques and strategies for getting the best results from AI chatbots like Chat GPT.

What are the different levels of prompt engineering mentioned in the script?

The script mentions 10 levels of prompt engineering, starting from basic requests to expert techniques, including basic formatting, focusing requests, giving examples, self-reflection, using system prompts, personas, chain of thought, self-prompting, and the co-star framework.

Why is basic formatting important in prompt engineering?

Basic formatting, such as using dashes to separate sections, can greatly help AI chatbots understand the different parts of a prompt, especially as the prompts become more complex.

What is the significance of being polite in your prompts according to the script?

The script suggests that being polite in prompts can help improve the accuracy of responses from large language models, as some research indicates that politeness may enhance their performance.

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

Appealing to intense emotions, like saying the response is important for a job or showing it to a loved one, can improve the responses of large language models by making the AI more invested in getting the answer right.

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

The purpose of being clear and focused in level three is to ensure that the AI chatbot understands exactly what information is needed and how it should be presented, reducing ambiguity and improving the quality of the response.

What is the 'fuse shot' technique in prompt engineering?

The 'fuse shot' technique, mentioned in the context of giving examples, involves providing the AI chatbot with example inputs and outputs to guide it towards the desired response format and content.

How does self-reflection help in prompt engineering?

Self-reflection, where the AI is asked if it missed anything, leverages the AI's strength in evaluation over generation, helping to ensure that no important information is omitted in the response.

What is the 'system prompt' and how is it used?

The 'system prompt' is a set of instructions that guides the AI chatbot to answer in a specific way. It involves providing context about the user and their preferences to tailor the AI's responses accordingly.

What is the benefit of using personas in prompt engineering?

Using personas can improve the accuracy of the AI's responses by 6 to 20%, as it helps the AI to understand the context and expertise required for the specific task at hand.

How does the 'chain of thought' technique work?

The 'chain of thought' technique involves asking the AI to explain its thought process step by step, which can help it handle complex problems more effectively and provide better outcomes.

What is the concept of self-prompting in prompt engineering?

Self-prompting is a technique where the AI is asked to create its own prompt to get the desired answer, leveraging the AI's ability to understand and generate prompts better than humans in some cases.

Can you explain the co-star framework mentioned in the script?

The co-star framework is an organizational method for prompts, where each letter stands for a specific part: Context, Objective, Style, Tone, Audience, and Response. It guides the AI chatbot to answer exactly how the user wants by providing detailed instructions for each part.

How does the co-star framework improve the quality of responses from AI chatbots?

The co-star framework improves response quality by providing a structured approach that covers all aspects of the prompt, ensuring that the AI chatbot has all the necessary information to generate a response that meets the user's expectations.