Resources
Resources
Prompt engineering is all about improving how we interact with AI, making it easier to get useful, accurate, or even creative responses. Imagine it like asking the perfect question to a friend or student—how you phrase it really affects the answer you get back. The same goes for AI: crafting the right prompt can make all the difference. Whether you're new to AI or just curious about how it works, learning to shape these prompts opens up a world of possibilities. It’s like having a conversation with technology, and the better you guide it, the more helpful and insightful it becomes.
Here we will provide you with an overview of some of the most used and most basic prompting techniques for getting good quality answers from a Large Language Model (LLM). For going deeper, please refer to the section below: "Resources for Going Deeper".
Zero-shot prompting is one of the simplest techniques for interacting with AI. In this method, you give the AI a single, clear instruction without providing any examples. The AI then uses its built-in understanding to generate a response based solely on the prompt. It's called "zero-shot" because the AI responds without any prior examples, much like answering a question without hints or additional context.
For example, if you’re using an AI to write an email, you might say, "Write a polite email asking for a meeting time next week." The AI will attempt to understand the instruction and create a response that fits the scenario based on its vast knowledge base. Zero-shot prompting is useful for straightforward tasks where you expect the AI to infer what’s needed without much context.
The limitation of zero-shot prompting is that if the task is too complex or nuanced, the AI might not fully grasp the request. This technique is most effective when the task is simple or general, and the instructions are clear. For beginners, zero-shot prompting offers an easy way to start using AI, as it requires minimal setup or training.
Example
Prompt:
Write a polite email asking for a meeting time next week.
AI-Generated Response:
Subject: Request for Meeting Next Week
Dear [Recipient],
I hope this message finds you well. I would like to schedule a meeting with you sometime next week to discuss [topic]. Please let me know your availability, and I will do my best to accommodate.
Looking forward to your response.
Best regards,
[Your Name]
Few-shot prompting builds on zero-shot prompting by providing the AI with a small number of examples, typically between two and five. These examples help guide the AI toward the desired structure, tone, or format for its response. By showing the AI what kind of output you’re expecting, you increase the likelihood of receiving more tailored, accurate responses.
For instance, let’s say you want the AI to help write customer support responses. Instead of just asking it to generate a response, you would provide a few example interactions, each with a specific format. This gives the AI a clearer idea of how to respond to similar queries.
Few-shot prompting is helpful when you need the AI to follow a specific pattern or maintain a certain style. It’s more guided than zero-shot prompting but still leaves room for flexibility, as the AI can adapt to slightly different requests based on the examples provided.
Example
Prompt:
Example 1:
Customer: "I need help with my order."
Support: "We’re happy to assist. Can you provide your order number?"
Example 2:
Customer: "I didn’t receive my package."
Support: "Sorry to hear that! Let’s check the status. Can you share your tracking number?"
Now respond to this:
Customer: "The item I received is damaged."
AI-Generated Response:
“Support: I’m really sorry to hear that your item arrived damaged. Please send us a picture of the item, and we’ll arrange a replacement or refund right away.”
Chain of thought prompting encourages the AI to reason through a problem step by step, rather than immediately jumping to an answer. This is particularly useful for tasks that involve logic, multi-step reasoning, or problem-solving. Instead of providing a single answer, the AI is prompted to explain its thought process, leading to more thorough and accurate responses.
For example, if you asked the AI to solve a math problem, you might want it to show how it arrived at the answer, rather than just giving the final result. This method mimics how humans break down complex tasks into smaller, manageable steps, which ensures clarity and reduces the chance of errors.
Chain of thought prompting is especially useful for tasks that involve complex decision-making, problem-solving, or analysis. By walking the AI through its thought process, you gain not only a correct answer but also insight into how it reached that conclusion.
Example
Prompt:
Solve 24 times 15 by breaking it down step by step.
AI-Generated Response:
“First, let’s break down the multiplication:
24 can be broken down into 20 + 4.
Now, let’s multiply each part by 15.
20 times 15 equals 300.
4 times 15 equals 60.
Finally, adding 300 and 60 gives us the answer: 360.
So, 24 times 15 equals 360.”
Prompt engineering is a skill that becomes more valuable the deeper you dive into it, offering ways to improve how we interact with AI tools. Whether you're just starting out or looking to refine your expertise, there are plenty of resources available to help. Some focus on the basics, guiding you through the foundational concepts, while others offer hands-on exercises that let you practice what you've learned. This list covers a range of options—from beginner-friendly introductions to more advanced guides—so you can choose the resource that best fits your level and learning style.
OpenAI’s Introduction to Prompt Engineering - A beginner-friendly guide from OpenAI, this resource walks you through the basics of prompt design and offers examples of how different prompts can change the AI’s responses.
Learn Prompting - A community-driven, interactive resource that starts with beginner concepts and goes deep into advanced techniques. It includes hands-on examples and covers prompt optimization for various AI models.
Prompt Engineering Guide on GitHub - This open-source, in-depth guide hosted on GitHub compiles a wide range of resources, including research papers, tutorials, and prompt examples to help you build expertise at your own pace.
DeepLearning. AI’s Prompt Engineering for Developers - This course is designed for developers but is accessible to anyone. It offers a structured learning path, focusing on practical applications and hands-on exercises to improve your prompt skills.
YouTube Channel: AI Explained - Prompt Engineering - A playlist of videos that breaks down different techniques for prompt crafting. These short, focused lessons are great for visual learners who want quick, clear explanations.
Prompt Engineering for Conversational AI - An article that offers a detailed look at how prompt engineering works in conversational AI models. It’s particularly useful for those interested in natural language processing (NLP).
EleutherAI’s GPT-Neo Tutorial - A Jupyter notebook tutorial from EleutherAI that provides a hands-on introduction to generating text with GPT-Neo, an open-source AI model. This is great for learners who want to experiment with prompts in real time.
Hugging Face's Course on Prompt Engineering - Hugging Face’s course offers a dedicated chapter on prompt engineering, with interactive examples and a clear progression from beginner to more advanced techniques.
Prompt Engineering: The Art of Getting What You Want from AI - This blog post offers a straightforward, easy-to-understand introduction to prompt engineering, including tips and examples for getting better results from AI.
Prompt Layer - A hands-on tool that allows you to experiment with different prompts and models in real time, providing a visual interface to tweak and optimize your prompts. Ideal for those who learn best by doing.
These resources provide a comprehensive path to mastering prompt engineering, regardless of your starting point. Whether you're just beginning or looking to deepen your understanding, you’ll find something here to help sharpen your skills.