Learn "ChatGPT Prompt Engineering for Developers" in 10 mins

Learn "ChatGPT Prompt Engineering for Developers" in 10 mins

This blog aims to provide readers with a concise summary of "ChatGPT Prompt Engineering for Developers," a course developed by Isa Fulford and Andrew Ng. If you're interested in the course but don't have 1.5 hours to spare, you can get a quick overview by reading this blog in just 10 minutes.
I used the technics I learned from the course and summarized the transcript. Here is the prompt. Check it out:
Video Summarizer GPT's thumbnail

Video Summarizer GPT

This chatGPT prompt summarize the video transcript and give you a easy-to-understand overview of the video.

summary

💡Section 1: ChatGPT Prompt Engineering for Developers ' Introduction

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.

👓Section 2: ChatGPT Prompt Engineering for Developers ' Guidelines

In this section, Isa discusses two key principles for effective prompting in language models like ChatGPT:
  1. Write clear and specific instructions: To get the desired output, provide clear and specific instructions. Tactics to achieve this include:
    • Using delimiters to separate distinct parts of the input
    • Asking for a structured output like HTML or JSON
    • Asking the model to check whether conditions are satisfied before proceeding
    • Employing few-shot prompting by providing examples of successful task executions
  2. Give the model time to think: If the model is making reasoning errors or incorrect conclusions, try reframing the query to request a chain of relevant reasoning before providing the final answer. Instructing the model to think longer about a problem can help improve the output quality.
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.

☕️Section 3: ChatGPT Prompt Engineering for Developers ' iterative

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.

🖥Section 4: ChatGPT Prompt Engineering for Developers ' summarizing

This section demonstrates how to use large language models like ChatGPT to summarize text. Summarizing helps process large volumes of information quickly and efficiently.
  1. The course starts with a running example of summarizing a product review.
  2. To create a summary, you can provide a prompt to the model that specifies the desired length of the summary, such as 30 words.
  3. For a more specific purpose, you can modify the prompt to reflect a particular aspect, like shipping or pricing.
  4. To extract information, ask the model to extract relevant information instead of summarizing it.
  5. The lesson provides a concrete example of summarizing multiple reviews using a for loop, which can be helpful when dealing with large volumes of text.
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.

⌨️Section 5: ChatGPT Prompt Engineering for Developers ' inferring

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.
  1. Sentiment Analysis: By writing a prompt, users can easily classify the sentiment of a text (positive or negative) without the need for traditional machine learning workflows.
  2. Emotion Recognition: The model can be used to identify specific emotions expressed in a text or to determine if a particular emotion, such as anger, is present.
  3. Information Extraction: The model can extract information such as item purchased and brand from a text. Multiple fields can be extracted using a single prompt.
  4. Topic Inference: The model can determine the topics discussed in a text and can help index different topics in a collection of articles using a zero-shot learning approach.
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.

✉️Section 6: ChatGPT Prompt Engineering for Developers ' Transforming

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:
  1. Translation: ChatGPT can translate text between hundreds of languages with varying proficiency levels. It can handle single or multiple translations, and even formal and informal versions of translations.
  2. Tone Transformation: ChatGPT can help produce text in different tones, such as converting slang to a formal business letter.
  3. Format Conversion: ChatGPT can translate between different formats, like JSON to HTML or XML, and markdown.
  4. Proofreading: ChatGPT can correct spelling and grammar errors, making it useful for proofreading text, especially in non-native languages.
The course provides examples for each application, demonstrating how to iteratively develop prompts to achieve the desired output.

📁Section 7: ChatGPT Prompt Engineering for Developers ' Expending

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:
  • Use helper functions to manage messages.
  • Provide context in messages for the model to draw from.
  • Customize the chatbot's behavior and persona using system messages.
  • Adjust the temperature setting for more predictable outputs.

Fafa's avatar
Fafa
Entrepreneur, Engineer, Product, AI enthusiast

Home
Explore
Chat
LeaderBoard
Me