A team of computer scientists, engineers, mathematicians and cognitive scientists have developed an open-source evaluation platform called CheckMate, which allows human users to interact with and evaluate the performance of large language models (LLMs). The researchers tested CheckMate in an experiment where human participants used three LLMs – InstructGPT, ChatGPT and GPT-4 – as assistants for solving undergraduate-level mathematics problems. The team studied how well LLMs can assist participants in solving problems. Despite a generally positive correlation between a chatbot's correctness and perceived helpfulness, the researchers also found instances where the LLMs were incorrect, but still useful for the participants. However, certain incorrect LLM outputs were thought to be correct by participants.

With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice. They have created an urgent need for educators to rethink pedagogical approaches and teaching strategies for newly emerging skill sets. Traditional approaches to learning programming have focused on frequent and repeated practice at writing code. The ease with which code can now be generated has resulted in a shift in focus towards reading, understanding and evaluating LLM-generated code. In parallel with this shift, a new essential skill is emerging -- the ability to construct good prompts for code-generating models. This paper introduces a novel pedagogical concept known as a `Prompt Problem', designed to help students learn how to craft effective prompts for LLMs. A Prompt Problem challenges a student to create a natural language prompt that leads an LLM to produce the correct code for a specific problem. To support the delivery of Prompt Problems at scale, in this paper we also present a novel tool called Promptly which hosts a repository of Prompt Problems and automates the evaluation of prompt-generated code. We report empirical findings from a field study in which Promptly was deployed in a first-year Python programming course (n=54). We explore student interactions with the tool and their perceptions of the Prompt Problem concept. We found that Promptly was largely well-received by students for its ability to engage their computational thinking skills and expose them to new programming constructs. We also discuss avenues for future work, including variations on the design of Prompt Problems and the need to study their integration into the curriculum and teaching practice.

Welcome to the world of ChatGPT, If you're new to ChatGPT, prompts are your trusty sidekicks, enabling you to communicate with this AI language model. They play a crucial role in getting meaningful responses and unlocking the true potential of ChatGPT. So what are ChatGPT prompts exactly? ChatGPT prompts are a way to interact with the AI model by giving it specific instructions or questions. They come in different classes, including question, statement, and opinion prompts. They are useful for various purposes, such as content creation, customer service, and education. Let's break it down, shall we? Think of prompts as conversation starters or cues.

Are you a new user of ChatGPT struggling to create effective ChatGPT prompts? Do you find it challenging to generate the desired outcome from your prompts? This beginner/intermediate course will teach you how to craft ingenious and highly effective prompts for Chat GPT by utilizing various prompt structures. Our expert prompt engineers provide numerous examples and case studies, allowing you to learn better and create winning ChatGPT prompts for more meaningful conversations with ChatGPT. In this comprehensive guide to prompt engineering, you'll learn the essential concepts and techniques required to create powerful ChatGPT prompts.

Given the new oracle that is ChatGPT, you may often find yourself tasked with creating prompts for various applications. One of the most significant challenges in this regard is crafting prompts that effectively communicate your requirements and elicit the desired response. In this article, I will provide a comprehensive guide on how to write high-quality prompts for software development, specifically for the ChatGPT language model. Our aim is to help you improve your skills as a prompt engineer, moving beyond generic advice and offering practical tips and examples. To create effective prompts, it is essential to understand the AI language model you are working with.

Is prompt engineering a process that tries to get accurate, logical, and consistent answers from an AI language model? Or is it a way to find the faults in a language model and then fix them to achieve the perfect artificial intelligence model, which kills "prompt engineering?" In this article, we'll concentrate on ChatGPT because it is the most popular model at the moment. But just in case this AI tool is new to you, I suggest you read our "ChatGPT for Beginners" article first. We'll also look at prompts for image generators like DALLE 2. I have written a few articles about this LLM (large language model) and learned that it is not so smart.

Artificial intelligence, particularly natural language processing, has a notion called prompt engineering (NLP). In prompt engineering, the job description is included explicitly in the input, such as a question, instead of being provided implicitly. Typically, prompt engineering involves transforming one or more tasks into a prompt-based dataset and "prompt-based learning"--also known as "prompt learning"--to train a language model. Prompt engineering, also known as "prefix-tuning" or "prompt tuning," is a method wherein a big, "frozen" pretrained language model is used, and just the prompt's representation is learned. Developing the ChatGPT Tool, GPT-2, and GPT-3 language models was crucial for prompt engineering.

Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are natural language. In this paper, we investigate common attributes shared by effective prompts. We first propose a human readable prompt tuning method (F LUENT P ROMPT) based on Langevin dynamics that incorporates a fluency constraint to find a diverse distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of label words. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong baselines by an average of 7.0% accuracy across three tasks.