ChatGPT is a user-friendly chatbot platform that provides a fun and engaging way for people to communicate with each other. It is designed to help people connect with each other by providing a platform for them to chat about various topics.

ChatGPT is powered by state-of-the-art artificial intelligence technology that allows it to understand and respond to users' queries and requests. The platform can be accessed through various channels, such as Facebook Messenger, Telegram, and Slack.

ChatGPT is not just a chatbot platform. It is also a community of users who share interests and ideas. Users can join various groups and channels on the platform to discuss topics they are passionate about.

Whether you want to chat with friends, get advice from experts, or simply have fun, ChatGPT is the perfect platform for you. So why not give it a try today and start chatting with people from all around the world!

I like easy ways to remember how to write my prompts.

PACO : Person | Action | Context | Output

ChatGPT can do many things, including the following:

Coming up with good question prompts for ChatGPT can be challenging, but some general principles and strategies can help guide you in the process.

Prompt Engineering a step-by-step process to communicate with ChatGPT

Communication with AI is crucial and understanding how to communicate with it effectively is helpful. The entire communication process revolves around writing commands which are referred to as prompts.

With that said, we can easily define prompt engineering as the step-by-step process of creating inputs that determine the output to be generated by an AI language model.

High quality inputs will result in better output. Similarly, poorly defined prompts will lead to inaccurate responses or responses that might negatively impact the user. After all, "With great power comes great responsibility".

Prompt engineering cuts across different applications, including chatbots, content generation tools, language translation tools, and virtual assistants. But you might be wondering how AI technology generates its responses. Let’s find out in the next section.

AI language models such as GPT-4 rely on deep learning algorithms and natural language processing (NLP) to fully understand human language.

All this is made possible through training that consists of large datasets. These datasets include articles, books, journals, reports, and so on. This helps the language models develop their language understanding capabilities. With the data, the model is fine-tuned in a way that enables it to respond to particular tasks assigned to it.

Depending on the language model, there are two main learning methods – supervised or unsupervised learning.

Supervised learning is where the model uses a labeled dataset where the data is already tagged with the right answers. In unsupervised learning, the model uses unlabeled datasets, meaning the model has to analyze the data for possible and accurate responses. Models like GPT-4 use the unsupervised learning technique to give responses.

The model has the ability to generate text based on the prompt given. This process is referred to as language modeling, and it's the foundation of many AI language applications. Learn more about Supervised vs Unsupervised Learning from IBM.

At this point, you should understand that the performance of an AI language model mainly depends on the quality and quantity of the training data. Training the model with tons of data from different sources will help the model understand human language including grammar, syntax, and semantics.

Note that, irrespective of the quantity of data used to train these models, there will always be challenges when it comes to understanding natural language. After all, this is an artificial being and understanding things like sarcasm, irony, or human feelings can be difficult for an AI model to interpret.

Now that we have an understanding of how the AI language model works, let's look at different prompt categories that are available to help us communicate with the models.

You can use prompts to ensure smooth communication with AI language models. The first step to writing quality prompts is understanding their different classifications so you can easily structure the prompts with a given target response in mind.

Some of the major prompt categories include:

An example of a role-based prompt is:

This prompt should also include what you intend to learn, the intended learning period, and your goal for learning. Remember that the more details you give, the more tailored results you will get.

Now that we have covered the different prompt categories, let's look at how you can craft good prompts. To help you understand better, we’ll go through different prompt engineering frames that optimize the responses we get by providing clear queries meant for NLP.

You should keep the following in mind when creating prompts:

Using a prompt framework when creating prompts for ChatGPT. Frameworks provides structure and clarity to the prompt creation process. It breaks prompt creation process into clear and distinct steps. I created the below framework (CRISPE) for my own use and experimentation of ChatGPT.

The final prompt being:

Act as an expert on software development on the topic of machine learning frameworks, and an expert blog writer. The audience for this blog is technical professionals who are interested in learning about the latest advancements in machine learning. Provide a comprehensive overview of the most popular machine learning frameworks, including their strengths and weaknesses. Include real-life examples and case studies to illustrate how these frameworks have been successfully used in various industries. When responding, use a mix of the writing styles of Andrej Karpathy, Francois Chollet, Jeremy Howard, and Yann LeCun.

I would refine this by saying Give me another example or Give me multiple examples and other prompts below (under Prompt Refinement).

Super Prompts are a feature of GPT-4 that allow users to give more specific instructions to the model. By entering a prompt with specific instructions, users can guide the model to produce more accurate and relevant responses. For example, a prompt like "Write a paragraph about the benefits of solar energy" will likely produce a more focused response than a more general prompt like "Write something about energy." Super Prompts can be especially useful for chatbots, where the accuracy and relevance of responses are especially important.