Day 2 | Different types of Prompting | Prompt Engineering Zero to Hero (5 Days)
TLDRIn this engaging session, the focus is on prompt engineering and its significant role in refining AI models like ChatGPT, making interactions more efficient. The session delves into various types of prompting, including zero-shot, one-shot, and few-shot prompting, and their applications. It also introduces Chain of Thought prompting and the importance of clear, concise language. The practical aspect involves creating a virtual health assistant project, emphasizing the step-by-step approach and the integration of custom instructions for better output. The session concludes with a lively quiz, reinforcing the day's learnings and encouraging active participation.
Takeaways
- ๐ The class is about Prompt Engineering, focusing on how to effectively communicate with AI models through prompts.
- ๐ฏ There will be a quiz session around 7:30 PM based on the topics covered in the previous session, with the top three winners receiving recognition on social media.
- ๐ก Prompt Engineering is a process of designing and optimizing prompts used in natural language processing models like ChatGPT.
- ๐ The session will be practical, diving deep into understanding the different types of prompts and how they can be used to get desired solutions from AI models.
- ๐ Three major principles of prompt engineering are: be specific, work in a step-by-step form, and reiterate as much as possible for better solutions.
- ๐ A good prompt should have clear and concise language, use personas for specific outputs, provide information and examples, and specify the task for the AI model.
- ๐ Prompt framing involves providing initial inputs to the model before generating a response, which helps in defining the format and structure of the output.
- ๐ Examples of different types of prompting include zero-shot, one-shot, few-shot, chain of thought, and fill-in-the-blank prompting.
- ๐ค The importance of using the right type of prompting is emphasized for achieving the best output from AI tools, regardless of the specific tool being used.
- ๐ The session also covered how to start writing prompts effectively, including using formulas and providing clear instructions to the AI model.
- ๐ The class ended with a quiz that was conducted through an online platform, with instructions provided on how to participate and what the winners would receive.
Q & A
What is the main focus of the class on prompt engineering?
-The main focus of the class is to dive deep into understanding what prompt engineering is, its different types, and how it can be used effectively to get the desired solutions from AI models.
What are the three major principles of prompt engineering?
-The three major principles of prompt engineering are: 1) Be specific with your questions, 2) Break down your prompts into small pieces, and 3) Reiterate as much as you can.
How can clear and concise language improve the effectiveness of a prompt?
-Using clear and concise language in a prompt ensures that the AI model can better understand the question, which in turn increases the likelihood of receiving the desired output.
What is the role of personas in prompt engineering?
-Personas help define a specific role or character that the AI model should adopt while responding to a prompt. This can influence the tone, style, and content of the AI's response to better match the user's expectations.
What is meant by 'prompt framing' in the context of AI models?
-Prompt framing refers to the practice of providing initial inputs or guidelines to the AI model before generating a response. This helps shape the format and content of the output according to the user's requirements.
How can providing examples in a prompt benefit the AI model's response?
-Providing examples in a prompt can help the AI model understand the context and desired outcome more clearly, leading to more accurate and relevant responses.
What is 'Chain of Thought' prompting?
-Chain of Thought prompting is a technique where the AI model is asked to provide a step-by-step explanation or reasoning for its answer, rather than just giving a direct response.
How does 'ask before you answer' prompting work?
-In 'ask before you answer' prompting, the AI model is instructed to seek clarification or additional information from the user if something is unclear, before providing an answer. This ensures that the response is tailored to the specific needs of the query.
What is the purpose of 'fill in the blank' prompting?
-Fill in the blank prompting is a method that allows the user to focus on a specific aspect of a sentence or idea by leaving a blank space for the AI model to complete, encouraging deeper thinking and more targeted responses.
What are the limitations of using Chat GPT 3.5 compared to Chat GPT 4?
-Chat GPT 3.5 is trained on older datasets and has limitations in handling file inputs like CSV files. It also has word limits for prompts. Chat GPT 4, on the other hand, can handle file inputs and has been trained on newer datasets, offering more up-to-date information and capabilities.
Outlines
๐ค Introduction and Agenda Setting
The speaker begins by apologizing for a technical glitch and ensures their voice and screen are working. They set the agenda for the day, mentioning a quiz session scheduled for 7:30 PM based on the previous day's topics. The aim is to engage participants and encourage them to stay till the end for a chance to win and be recognized on social media platforms.
๐ Understanding Prompt Engineering
The speaker delves into the concept of prompt engineering, explaining it as a process of designing and optimizing prompts for natural language processing models. They emphasize the importance of being specific, breaking down tasks, and reiterating problems to get better solutions from AI models. The speaker also introduces the idea of using personas and providing clear examples to enhance the effectiveness of prompts.
๐ Writing Effective Prompts
The speaker discusses the qualities of a good prompt, highlighting the need for clear and concise language. They explain the role of personas in shaping the AI's response and the importance of providing examples. The speaker also emphasizes the need to define the task clearly for the AI to understand what is expected of it.
๐ Prompt Engineering Principles
The speaker outlines the three main principles of prompt engineering: being specific, working in steps, and reiterating. They explain how these principles help in crafting effective prompts that yield better results from AI models. The speaker also introduces the concept of prompt pruning, where the AI's output is defined before the question is asked.
๐ Main Prompting Steps
The speaker describes the main steps in prompting, which include defining the problem, using relevant keywords, writing the prompt, and testing and evaluating the output. They also discuss the concept of prompt pruning, providing examples of how it can be used to guide the AI's output format.
๐ก Starting Your Prompt
The speaker offers advice on how to start writing a prompt, suggesting various prompt starters for different scenarios. They provide examples of how to ask for elaboration on a topic, create a template, and brainstorm new ideas. The speaker emphasizes the importance of knowing how to ask and what to get from the AI.
๐ ๏ธ Prompt Frameworks and Types
The speaker explains different types of prompting, including zero-shot, one-shot, and few-shot prompting. They discuss the importance of prompt frameworks in achieving desired outputs and provide examples to illustrate the differences between these prompting types.
๐ค Chain of Thought Prompting
The speaker introduces chain of thought prompting, a method that encourages the AI to provide answers in a step-by-step format. They demonstrate how this approach can help understand the reasoning behind the AI's responses and provide a deeper understanding of the topic at hand.
๐ Tabular Format Prompting
The speaker discusses tabular format prompting, where the AI's response is organized into categories and presented in a table format. They explain how this method can enhance the descriptiveness of the answer and make it easier to understand and analyze.
๐ Ask Before Answering
The speaker describes the 'ask before answering' technique, where the AI is instructed to clarify any doubts before providing an answer. This method ensures that the AI understands the problem fully before offering a solution, leading to more accurate and relevant responses.
๐ Fill in the Blank Prompting
The speaker explains fill in the blank prompting, a method that allows for focused and deeper thinking on a specific aspect of a sentence or idea. This technique is presented as a flexible tool for learning and communication that can be adapted to various situations.
๐ฏ Project Planning with Prompts
The speaker illustrates how to use prompts for project planning, specifically for creating a virtual health assistant. They demonstrate the use of chain of thought and ask before answering techniques to guide the AI in providing a step-by-step guide for the project.
๐ Quiz Time and Summary
The speaker concludes the session with a quiz to engage the audience and review the day's learnings. They explain the rules of the quiz, encourage participation, and assure a solution to the technical lag issues experienced during the quiz. The speaker also reminds participants to practice what they've learned and looks forward to the next session.
Mindmap
Keywords
๐กPrompt Engineering
๐กChat GPT
๐กNatural Language Processing (NLP)
๐กQuiz
๐กInstagram
๐กReiteration
๐กPersona
๐กClear and Concise Language
๐กPractical Session
๐กChain of Thought Prompting
Highlights
Introduction to prompt engineering and its role in optimizing AI model responses.
Explanation of the three major principles of prompt engineering: be specific, work in steps, and reiterate.
Discussion on the importance of clear and concise language in prompts for better AI understanding.
The concept of using personas in prompts to guide AI models in providing specific types of responses.
The significance of providing examples and information in prompts to help AI models generate accurate outputs.
Explanation of how to define a problem and use relevant keywords and phrases to construct effective prompts.
Introduction to prompt pruning, a method of guiding AI models on the desired format of the output.
Demonstration of zero-shot, one-shot, and few-shot prompting techniques and their differences.
The concept of Chain of Thought prompting, which encourages AI models to think step by step and explain their reasoning.
Discussion on the practical applications of prompt engineering in everyday tasks and problem-solving.
Explanation of how to use custom instructions and frameworks to tailor AI responses to specific needs.
The importance of adapting and improving prompts based on the output received from AI models.
Overview of the quiz conducted to engage participants and test their understanding of prompt engineering concepts.
Instructions on how to participate in the quiz and the significance of fast and accurate responses.
Details on the rewards for quiz winners, including shoutouts on social platforms and the process for claiming prizes.
Conclusion of the session with a summary of key learnings and encouragement for participants to practice and join future sessions.