DeepLearning.AI Course - ChatGPT Prompt Engineering for Developers

The summary of the provided content:

This course on ChatGPT Prompt Engineering for Developers features Isa Fulford, a technical staff member at OpenAI. She has extensive experience with Large Language Models (LLMs) and has developed the ChatGPT Retrieval plugin and contributed to the OpenAI Cookbook. The course aims to teach developers how to use LLMs to build software applications, highlighting the potential and best practices of LLMs.

The course will cover prompting best practices, common use cases, and building chatbots with LLMs. It distinguishes between base LLMs, which predict the next word in a sequence, and instruction-tuned LLMs, which are trained to follow instructions and are generally more helpful, honest, and harmless. The latter is recommended for most practical applications.

The instructors acknowledge the contributions of several individuals from OpenAI and DeepLearning.ai who helped create the course materials. When using instruction-tuned LLMs, clear and specific instructions are critical for success, much like how one would give instructions to a smart but uninformed person. The course will provide examples of effective prompting and strategies to help LLMs generate desired outcomes.

Isa provides a tutorial on effective prompt engineering with language models like ChatGPT, emphasizing two key principles: writing clear and specific instructions, and allowing the model time to think. She uses the OpenAI Python library and the GPT 3.5 Turbo model to demonstrate various tactics for crafting prompts that yield desired results. These tactics include using delimiters to clearly separate text, asking for structured outputs (e.g., in JSON format), checking if conditions are met before completing a task, and providing examples through few-shot prompting. Isa also shows how to guide the model through complex tasks by specifying steps and instructing it to work out its own solutions before drawing conclusions. She cautions about the model’s limitations and tendency to hallucinate plausible but incorrect information, suggesting quoting relevant text to minimize this. The tutorial includes practical examples in a Jupyter Notebook and encourages viewers to experiment with the code and prompts themselves.

The passage describes the iterative process of developing prompts for large language models (LLMs) to build applications. It emphasizes that the first prompt rarely works perfectly, and the key to success is having a good process for refining prompts based on feedback from the model’s output. An example is provided where a prompt is created to help a marketing team write a product description for a chair. The prompt goes through several iterations to improve clarity, focus on technical details, control the length of the output, and include specific product IDs. This process involves writing a prompt, running it, analyzing the result, and then refining the prompt accordingly. The passage also touches on the importance of keeping best practices in mind, such as being clear and specific, and it suggests that even complex prompts are the result of multiple iterations. Finally, the passage encourages experimentation with different prompt variations using Jupyter notebooks and prepares the reader for the next video on using LLMs to summarize text.

This passage discusses the application of large language models to summarize text, which can be a valuable tool for reading and understanding large volumes of content quickly. The author demonstrates how to use the OpenAI API to create summaries of product reviews, which can be particularly useful for e-commerce websites dealing with numerous customer reviews. The text explains how prompts can be customized to generate summaries with a specific focus, such as feedback for the shipping or pricing departments. The author also distinguishes between summarizing and extracting information, with the latter being more focused and concise. Finally, the author presents a practical example of summarizing multiple reviews to create an easy-to-read dashboard that could allow users to quickly grasp overall customer sentiment without reading each review in detail. The next video promises to explore how language models can infer sentiments from text, such as distinguishing between positive and negative product reviews.

The video discusses how large language models can be used to perform various natural language processing tasks, such as sentiment analysis, emotion detection, and information extraction, through the use of prompts. The speaker illustrates how a single model can be prompted to analyze a product review for sentiment, determine if anger is expressed, extract items and brands, and even infer topics from a text. This approach is contrasted with traditional machine learning methods, which would require separate models for each task. The speaker demonstrates how to write prompts that produce both verbose and concise outputs, and how to extract multiple pieces of information with a single prompt. Additionally, the concept of zero-shot learning is introduced, where the model can classify topics within a text without prior labeled data. The video concludes by highlighting the efficiency and potential of large language models in quickly performing complex NLP tasks that would have previously taken much longer to develop. The next video will cover text transformation tasks, such as language translation.

Large language models can transform text into different formats, such as translating between languages, correcting grammar and spelling, and converting between data formats like HTML to JSON. The video demonstrates these capabilities using OpenAI’s language model, showing how it can handle translation across multiple languages, identify the language of a given text, and perform translations while considering formality levels. The video also showcases how the model can adapt tone in writing, convert data formats (e.g., from JSON to an HTML table), and proofread text, correcting grammatical and spelling errors. It can even enhance a text’s appeal and ensure it follows specific style guides, like APA, while converting it into markdown format. The video encourages iterative prompt development for more reliable transformations and concludes by mentioning the next topic on "Expanding" text responses.

The text discusses the concept of expanding text using a large language model, highlighting both positive uses, like brainstorming, and potential misuse, like generating spam. It urges responsible use of AI, emphasizing transparency when presenting AI-generated text to users. The video outlines how to use a language model to create personalized email responses based on customer reviews and sentiment. It introduces the concept of "temperature," a model parameter that affects the variety and unpredictability of the AI’s responses. Lower temperatures yield more predictable outcomes, while higher temperatures produce more varied and potentially creative outputs. The video suggests experimenting with different temperatures to see how the model’s responses change and indicates that the next video will cover creating a custom chatbot.

The content provided is a tutorial on how to create a custom chatbot using a large language model like ChatGPT. The video tutorial explains that ChatGPT is trained to handle a series of messages and generate responses, making it suitable for both multi-turn conversations and single-turn tasks. The author introduces helper functions for obtaining model completions and emphasizes the importance of system messages, which set the behavior and persona of the assistant without being visible to the user. The tutorial includes examples of system messages, user messages, and assistant messages, and shows how to maintain context within a conversation. Finally, viewers are encouraged to build their own chatbot named "OrderBot" for taking pizza orders, with the process involving the collection of user prompts and assistant responses.

This short course taught participants about effective prompting principles, such as providing clear and specific instructions and allowing the model time to think. It covered the process of iterative prompt development, essential for tailoring prompts to specific applications. The course also introduced key capabilities of large language models: summarizing, inferring, transforming, and expanding information, along with how to build a custom chatbot. The instructors encouraged learners to apply their new skills to create applications, regardless of scale, emphasizing the importance of responsible AI development and the potential for positive impact. The course aimed to equip participants with unique knowledge in a growing field, urging them to share their learning experience and look forward to building impactful projects.