Prompt Engineering 03: Understanding Prompting Techniques(2/2)

Focusing on Prompting Techniques(Zero Shot Chain of Thought、Least to Most Prompting, Dual Prompt Approach and Combining Techniques) in Prompt Engineering.

This article was produced with the help of AI, If there are mistakes, welcome to correct, I will correct in time

full lessons here👇:

Now let’s outline a detailed curriculum for Prompting Techniques.

1.1 Introduction to Prompting Techniques: Explanation of the importance and roles of diverse prompting techniques.

1.2 Zero Shot Chain of Thought Prompting: Deep dive into the concept of Zero Shot Chain of Thought Prompts and how they influence AI’s response.

1.3 Least to Most Prompting: Understanding the concept of Least to Most Prompting and situations where they are beneficial.

1.4 Dual Prompt Approach: Understanding the Dual Prompt Approach mechanism and its strategic value for controlling AI’s responses.

1.5 Combining Techniques: Learn how to combine different prompting techniques to maximize desired output from the AI.

1.6 Review and Assessments: Review of the key concepts covered and assessments to test understanding and application of Prompting Techniques.

Topic: 1.1 — Introduction to Prompting Techniques

Prompting techniques make a huge difference when it comes to using Language Learning Models like GPT-3. They help us in guiding the model’s output to our liking. Various prompting techniques are used depending upon the kind of requirements we have from the model.

Picture these techniques as a way to shape the response that we receive from an AI model. The quality of our prompts will often directly impact the quality of the outputs.

Let’s break down some of these techniques:

1. Role-based prompting: This technique assigns a role to the AI model which it needs to perform. For example, if you assign the role of an English Grammar checker to the AI, it starts to assess and correct grammatical mistakes.
2. Prompt chaining: Sometimes, the output from one prompt can be used as the next prompt. By using past responses from the AI, new prompts are made thus forming a chain of prompts. This can be really useful for maintaining longer and meaningful conversations.
3. Few-shot and Zero-shot prompting: These techniques mainly deal with the number of examples that are being used to shape your model’s behavior. Few-shot prompting makes use of several instances to teach the model about the task whereas Zero-shot doesn’t use any examples for its part.

As we delve more into each of these methods in the coming modules, you will realize that each method has its own strengths and it’s about finding the right method for the task at hand.

Remember, the correct utilization of these techniques can transform AI’s output from good to great. And that’s our goal!

Topic: 1.2 — Zero Shot Chain of Thought Prompting

Zero Shot Chain of Thought Prompting is an interesting and effective technique when it comes to instructing AI.

The term ‘Zero Shot’ means that we are asking the model to perform a task without providing any examples of how to do so. Instead of showing the AI multiple samples as a point of reference, we specify the desired task and counting on the model’s pre-existing understanding to guide its response.

In a nutshell, we’re asking the model to apply its learned knowledge and reason out the solution, based solely on the information given in the prompt.

For instance, if we prompt the AI: ‘Translate the following English text to French: Hello, how are you?’, we aren’t providing an example of how to perform a translation. Rather, we trust the AI’s base language understanding and its previous knowledge about translation tasks.

This technique is like a chain where every piece has its own place and significance. ‘Chain of Thought’ prompts can be more conversational in nature. We’ve been doing it throughout this lesson, where my responses to your commands are influenced by all of our previous exchanges, forming a chain of thought.

Now, remember that this technique, while useful, isn’t always the ideal choice. It may sometimes lead to less accurate results, especially for more complex or very specific tasks, or if the model doesn’t already have a pertinent understanding of the topic.

Question: How does Zero Shot Chain of Thought Prompting differ from other prompting techniques?

Answer: Zero Shot Chain of Thought Prompting is unique in the sense that it focuses more on the model’s built-in training and doesn’t rely on checking multiple examples to guide its response for a specific task. Here’s how it differs:

1. Contrast with Few-Shot Prompting: For Few-Shot Prompting, you provide several examples from which the AI model can learn the pattern or response format. In contrast, Zero Shot provides no such examples, making it rely completely on the previous data it has been trained with.
2. Comparison with Role-based Prompting: In Role-based Prompting, we specifically instruct the AI to play a certain role or character. But with Zero Shot Chain of Thought Prompting, it’s more about a progression or chain of concepts that we guide the AI through.
3. Comparison with Prompt Chaining: While both involve maintaining a kind of continuity, Prompt Chaining usually refers to the way context is maintained from one AI response to the next. With Zero Shot Chain of Thought, there’s a similar continuity but with the added challenge of not having any specific examples for guidance.

It’s important to note that while they differ in their techniques, all of these methods have the same goal: to guide the AI and obtain the best output for our particular use-case.

Topic 1.3 Least to Most Prompting

The term ‘Least to Most Prompting’ involves using the least amount of prompt to solicit a response from an AI, gradually increasing the information given if the AI doesn’t respond accurately. This is based on encouraging the AI to rely more on its learned knowledge without overly guiding it.

The approach can be summarized in a sequence of steps:

1. Start with a broad, open-ended prompt: This encourages the maximum autonomy for the AI.
2. If the response is not accurate or fitting, make your prompt more specific: You can guide the AI a little in the direction you want it to.
3. If the problem persists, make your prompt very explicit: This is the most-guided step, where you specify your requirement in detail.

This method allows for increasing the support provided based on the AI’s need, much in the way a teacher might scaffold support for a learner.

Let’s consider an example where we want an AI model to write about the importance of learning foreign languages. We’ll start with the broadest prompt and gradually make them more specific if needed.

1. Least Prompt: Write an essay.
2. This is a very open-ended prompt. But it’s not specific at all and doesn’t guide the AI on what the topic of the essay should be. Therefore, it’s likely the AI won’t write about the desired subject.
3. More Specific Prompt: Write an essay on education.
4. This prompt narrows the AI’s focus a bit, prompting it to write about education. However, it’s still broad and doesn’t guarantee the AI will mention foreign language learning.
5. Most Specific Prompt: Write an essay discussing the importance and benefits of learning foreign languages.
6. In this prompt, we explicitly state the topic we want the AI to write about. This is the most directed prompt, and we would, therefore, expect the AI to effectively generate an essay on the importance of foreign language learning.

The idea here is to try being as minimalist as possible with the prompts, letting the AI exercise its knowledge, but narrowing down when necessary to guide it towards producing the desired output.

Moving on, using Least to Most Prompting can have several advantages:

1. It promotes AI’s autonomy and encourages the broad use of learned knowledge.
2. It allows the model to showcase unexpected capabilities or generate results you might not otherwise know to ask for.
3. SIPrompts can be gradually increased to guide the model’s responses more effectively if needed.

Now, a common misconception here is that giving the broadest possible prompt is always best. In fact, there are times when a more specific prompting may be more useful. For example, when accuracy is crucial, when time is limited, or when the user demands are precise.

Least to Most Prompting is just one technique in a suite of many you can use in different scenarios depending on your specific needs.

Topic: 1.4 Dual Prompt Approach

The Dual Prompt Approach is a technique that involves using both a ‘task prompt’ and a ‘system prompt’.

The ‘task prompt’ includes the question or task you want the AI model to execute. On the other hand, ‘system prompt’ involves instructing the AI about how to approach or structure the response to the ‘task prompt’.

So, consider this scenario. You are asking the AI to generate an argument for promoting environmental sustainability measures in businesses. Here’s how the Dual Prompt Approach comes in handy:

Task Prompt: ‘Generate an argument for promoting environmental sustainability measures in businesses.’

System Prompt: ‘Start with an engaging opening, then outline the problem, discuss the benefits of addressing the issue, provide examples of businesses that have benefited from these measures, and end with a call to action.’

By using this approach, you not only define what you want from the AI, but also guide it on how to structure its response. Now, you may ask why would one consider this approach? Great question…

Let’s continue with our in-depth exploration into the Dual Prompt Approach.

The Dual Prompt Approach has important implications in controlling the output from an AI, and provides various benefits:

1. Improves response structure: The most obvious advantage of this approach is that it helps ensure the AI’s output follows a certain structure, creating a more logically arranged and contextually aware response.
2. Balances creativity and control: While less restrictive prompts can sometimes lead to surprising and creative answers, they can also result in responses that wander off-topic or neglect to include important elements. By adding a ‘system prompt’, you can maintain some of the benefits of open-endedness while still guiding the AI towards a desirable outcome.
3. Enhance relevance: System prompts also allow us to steer the AI towards including content that may not necessarily be implied by the task prompt. It offers an additional layer of customization that further aligns the AI with our preferences and requirements.

let’s take a look at some examples to see the Dual Prompt Approach in action!

Example 1: Writing a Blog Post

Task Prompt: ‘Write a blog post about the health benefits of regular exercise.’

System Prompt: ‘Start with an engaging introductory paragraph that mentions the main benefits of exercise, then delve into each benefit in separate sections, providing scientific evidence to back up each point. End with a motivational concluding paragraph that encourages the reader to start exercising regularly.’

In this example, the system prompt ensures that the AI’s output is well structured and includes motivational elements that might not be included with the task prompt alone.

Example 2: Generating a Recipe

Task Prompt: ‘Generate a recipe for a classic Italian lasagna.’

System Prompt: ‘Start with a list of ingredients, followed by a step-by-step guide on how to prepare the dish. Include tips for each step and suggest some variations to the recipe at the end.’

In this case, the system prompt not only guides the structure of the recipe, but also encourages the inclusion of extra helpful details like cooking tips and recipe variations.

Dual prompts can be an extremely useful tool in your AI toolbox, helping to maximize the usefulness and relevance of the AI’s output! I hope this helps clarify the concept.

Topic: 1.5 Combining Techniques

Our journey through the world of prompting techniques now brings us to an impressive concept known as Combining Techniques. Sometimes, a single approach might not yield the results you want, and that’s where the power of combination comes into play!

Combining Techniques refers to the practice of blending different prompting techniques together to develop a more efficient mix that elevates the AI’s output. This can be very beneficial in several ways:

• It allows us to leverage the strengths of different techniques to counterbalance the weaknesses of others.
• It maximizes the chance of obtaining the desired output.
• It promotes AI versatility by creating more engaging and dynamic interactions.

To give you a taste of how it works, let’s elaborate with an example. Imagine using a Chain of Thought Prompt with a Role Prompt. This allows you to lead the AI through a planned sequence of ideas (Chain of Thought Prompt) while maintaining a specific communication style that aligns with the role the AI is asked to emulate (Role Prompt).

Remember, this technique requires practice and experimentation to master. It’s a balancing act. But worry not, we’ll go over some examples in the next part! Ready to dive in further?

let’s proceed and explore this through a couple of examples.

Example 1: Historical Fiction Role-Play

Suppose you want the AI to generate a dialogue that could occur between two historical figures, say Martin Luther King Jr. and Gandhi, in a modern context (like a podcast discussion!). Combining a role-play prompt with the setting using least-to-most prompting, you might construct the following:

Task Prompt: “Role-play a conversation between Martin Luther King Jr. and Gandhi, discussing modern issues like social media and its impact on non-violent protests.”

System Prompt: “Initiate the dialogue by setting the scene, introducing the characters. Have Gandhi raise the topic, then both of them take turns to share their views, building off each other’s points. Highlight their individual philosophies and how they may apply to the current societal context.”

Example 2: A Company Mission Statement

You’ve been asked to generate a mission statement for a company. Not just any company, but a futuristic one that plans to terraform Mars! You can combine multiple techniques such as role-play prompting to help the AI understand the role of the company, chain of thought prompting to guide how the statement should evolve, and least-to-most prompting to specify the detail level you want in the statement.

Task Prompt: “You are the CEO of a futuristic company, TerraFirma Inc., that aims to make Mars habitable for humans. Generate a mission statement that sums up our ambitious goal.”

System Prompt: “The statement should be succinct yet inspiring. Start with a broad, promising vision, then specify the actions TerraFirma will take to achieve this vision. Include an optimistic note on the future of humanity.”

These were just examples, and the potential combinations depend on your particular needs and creativity. Combining techniques allow you to tap into a rich range of AI responses while maintaining control over the output.

Topic: 1.6 Review and Assessments

During this stage, we’ll review the key concepts of what we’ve learned so far and perform a self-assessment. The aim here is to check your understanding and ensure that you can apply these concepts when crafting your prompts.

1. Let’s recall the main prompting techniques we’ve discussed:

• Zero-Shot Chain of Thought Prompting: We dove into this prompting technique which doesn’t require any example prompts to guide the AI — it uses the AI’s pre-existing knowledge to generate a response.
• Least to Most Prompting: Targets situations where the response complexity varies. Starts with minimum guidance and adds more details if necessary.
• Dual Prompt Approach: A strategic combination of Task Prompts and System Prompts to allow precise control over the model’s responses.
• Combining Techniques: We learned how combining diverse prompting techniques can lead to more tailored and maximized results.

1. Now, I’d like you to think about a few things:

• Which prompting technique do you think you would use most and why?
• Can you imagine a scenario where you would use each of the discussed techniques? Try coming up with different use-cases.
• Based on your learning, how would you approach the task of crafting a prompt to ensure a high-quality output?

After this review, we will perform a mini assessment to gauge your understanding.

Let’s proceed with the mini-assessment. For this part, I will provide you with three tasks. Your goal is to derive the most appropriate prompting technique based on the situation. Try to recall what we’ve learned from our lessons. If you have any questions, feel free to ask.

Task 1: You’re working on a creative project where you need to generate an engaging dialogue between two fictional characters. You want this dialogue to flow naturally but with control over its direction.

Task 2: You plan to use OpenAI for your research paper. You need it to explain a complex scientific phenomenon in a comprehensive manner, starting with its fundamentals.

Task 3: You’re developing a game and need to generate a backstory for a character. This is a new character type that doesn’t exist in traditional lores or mythologies.

Take your time to think about it. Once you’re ready, you can share your answers.

Try it yourself and slide down. Below are my answers:

Task 1: To generate dialogue between characters, we can use the Zero-Shot Chain of Thought Prompting technique. This is ideal for an organic, back-and-forth conversation since this technique allows the conversation to unfold naturally with the AI’s pre-existing knowledge base.

Task 2: For explaining a complex scientific concept in a comprehensive manner, starting from the very basics, we can use the Least to Most Prompting technique. By providing initial broad prompts and slowly refining them to add complexity, the AI can step-by-step go from the fundamental concept to a complex discussion.

Task 3: While generating a unique backstory for a new game character, using a Dual Prompt Approach could be quite beneficial. In this case, the task prompt could lay out the basic character information, and the system prompt could guide the style and creativity aspects of the character’s backstory.

These are suggested approaches based on our discussions. However, remember, there’s no one-size-fits-all in AI prompting. The best technique often depends on the specific requirements of your task.

I hope this review was helpful for you.