Learn "ChatGPT Prompt Engineering for Developers" in 10 mins

I used the technics I learned from the course and summarized the transcript. Here is the prompt. Check it out：

This course focuses on ChatGPT prompt engineering for developers. Isa Fulford, a member of OpenAI's technical staff, joins the teaching team. The course aims to teach how to use Large Language Models (LLMs) effectively in software development, covering prompting best practices and common use cases like summarizing, inferring, transforming, and expanding. Participants will also learn to build a chatbot using an LLM.

There are two types of LLMs: base LLMs and instruction-tuned LLMs. Base LLMs predict the next word based on text training data, while instruction-tuned LLMs follow instructions more effectively. Instruction-tuned LLMs have become more practical and are the focus of this course. The course materials are contributed by a team from OpenAI and DeepLearning.ai.

When using instruction-tuned LLMs, it's essential to give clear and specific instructions and allow the LLM time to think. The next video will delve deeper into these principles.

This course focuses on ChatGPT prompt engineering for developers. Isa Fulford, a member of OpenAI's technical staff, joins the teaching team. The course aims to teach how to use Large Language Models (LLMs) effectively in software development, covering prompting best practices and common use cases like summarizing, inferring, transforming, and expanding. Participants will also learn to build a chatbot using an LLM.

There are two types of LLMs: base LLMs and instruction-tuned LLMs. Base LLMs predict the next word based on text training data, while instruction-tuned LLMs follow instructions more effectively. Instruction-tuned LLMs have become more practical and are the focus of this course. The course materials are contributed by a team from OpenAI and DeepLearning.ai.

When using instruction-tuned LLMs, it's essential to give clear and specific instructions and allow the LLM time to think. The next video will delve deeper into these principles.

In this section, Isa discusses two key principles for effective prompting in language models like ChatGPT:

Isa demonstrates these principles using a Jupyter Notebook and the OpenAI API, using helper functions to interact with the API. She provides various examples to illustrate the importance of clear and specific instructions and the value of giving the model time to think. In the second part of the session, some tactics are discussed to improve the model's performance. The first tactic is to specify the steps required to complete a task, making it easier to parse the model's output. The second tactic is to instruct the model to work out its own solution before rushing to a conclusion, which can lead to more accurate responses. The session also discusses model limitations, such as the model's inability to perfectly memorize information and its tendency to fabricate plausible but false information, known as hallucinations. To reduce hallucinations, one tactic is to ask the model to find relevant quotes from a text and use those quotes to answer questions. This part concludes with an introduction to the next video, which will cover the iterative prompt development process.

In this section, the instructor emphasizes the importance of iteratively developing prompts for large language models. They explain that it's crucial to have a good process to refine prompts until they work effectively for the desired task. This iterative process is similar to machine learning development, where one starts with an idea, implements it, gets experimental results, and then refines the idea based on the analysis of the output. The instructor will use the task of summarizing a fact sheet for a chair as a running example in the course.

The instructor demonstrates the iterative process of prompt development using the example of creating a product description for a chair. They start with a simple prompt and gradually refine it to make the output more focused, shorter, and include specific information like product IDs. The instructor also shows how to request a specific output format, such as HTML, and highlights the importance of having a good process to develop prompts that work effectively for a particular application. For more mature applications, evaluating prompts against a larger set of examples can help optimize performance.

This section demonstrates how to use large language models like ChatGPT to summarize text. Summarizing helps process large volumes of information quickly and efficiently.

By using large language models like ChatGPT, you can create concise summaries that allow users to quickly understand the content of texts and make informed decisions.

This section covers the topic of inferring using large language models, which allows for various tasks like sentiment analysis, emotion recognition, and information extraction. The main advantages of these models are their ability to perform multiple tasks with a single API and their speed in application development.

The course also provides examples of how to format responses as JSON objects and suggests potential improvements for more robust systems. The video concludes by emphasizing the speed and efficiency with which large language models can handle complex NLP tasks, and the next video will focus on text transformation.

This section demonstrates various applications of large language models, specifically focusing on translation, tone transformation, format conversion, and proofreading. The course uses ChatGPT to showcase these capabilities and provides examples for each application:

The course provides examples for each application, demonstrating how to iteratively develop prompts to achieve the desired output.

This section focuses on the use of a large language model for generating personalized emails and highlights the importance of using these capabilities responsibly. The course introduces the concept of temperature as a parameter to control the variety of responses generated by the model. Higher temperatures result in more random outputs, while lower temperatures create more predictable responses.

The course provides an example of creating a custom email response to a customer review using the OpenAI Python package and a helper function called getCompletion. The response generated by the AI assistant depends on the sentiment of the review. In the example, the AI assistant thanks the customer for a positive or neutral review and apologizes for a negative one, suggesting that the customer reach out to customer service. The generated email is transparent, informing the user that it is written by an AI customer agent.

Finally, the course encourages experimenting with different temperature settings to see how the generated outputs vary. In the next video, the course will explore the Chat Completions Endpoint format and creating a custom chatbot using this format.

##Section 8: ChatGPT Prompt Engineering for Developers ' Chatbot https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/8/chatbot

In this online course, you learn how to create a custom chatbot using OpenAI's ChatGPT. The tutorial covers the components of the OpenAI ChatCompletions format, such as setting up the Python package, understanding user and assistant messages, and working with system messages to set chatbot behavior.

The course demonstrates creating a pizza-ordering chatbot called OrderBot, which collects user messages and assistant responses in a conversation. The chatbot's behavior is guided by a system message that instructs it to greet the customer, collect the order, and handle other essential aspects of the ordering process. By the end of the course, you will be able to create a JSON summary of the conversation, which can be submitted to an order system.

Remember to: