Free Course on ChatGPT Tip Engineering for Developers by Ernest Ng

What is the ChatGPT Tip Project for Developers?

The ChatGPT prompt project for developers isDeepLearning.AICo-presented with OpenAI, this course, designed for developers and presented by Isa Fulford, Andrew Ng, teaches how to quickly build powerful applications using Large Language Models (LLMs) and the OpenAI API. The course covers best practices for cue engineering, including tasks such as how to build custom chatbots, summarization, inference, text conversion, and scaling. Through nine video lessons and seven code examples, participants will learn the two main principles of effective prompt writing, practiced in the Jupyter notebook environment. The course is suitable for beginners and advanced machine learning engineers and requires only basic Python knowledge.

Course Objectives for ChatGPT Prompt Engineering for Developers

• Mastering the best practices of cue engineering: Learn how to write high-quality prompts that optimize the model's output.
• Explore new uses for LLM: Learn how to do application development with LLM, including building custom chatbots.
• Access to hands-on opportunities: Practicing with the OpenAI API based on actual authoring and iterative prompts.
• Understanding how LLM works: In-depth understanding of the operation mechanism of LLM to facilitate better use of its functions.
• Improve development efficiency: Learn how to use LLM to quickly implement otherwise time-consuming and labor-intensive features such as text summarization, sentiment analysis, translation, and more.

Course syllabus for ChatGPT prompted engineering for developers

• introductory: An introduction to the two main types of Large Language Models (LLMs): base LLMs and instruction fine-tuning LLMs, as well as to the characteristics of the models and application scenarios.
• guidebook: Learn the four main strategies for writing clear, unambiguous instructions and how to give models enough time to think and improve the accuracy and usefulness of their output.
• iteration (math.)Master the method of optimizing Prompt through continuous iteration, adjusting the model based on the results returned to get a more satisfactory output.
• summaries: Learn how to use ChatGPT Assist students in growing their summaries of text, including refining needs and understanding keyword differences to improve the accuracy of the summary.
• inference: Sentiment analysis and information extraction with ChatGPT, including recognizing sentiment and extracting information about entities, themes, etc., to enhance textual reasoning.
• conversions: Use ChatGPT for text conversion tasks such as translation, tone adjustment, formatting, and text checking to meet different text processing needs.
• extensions: Learn how to use ChatGPT to expand on text, such as automatically generating reply emails, and understand how temperature parameters affect output.
• chatbot: Learn to build a ChatGPT-based chatbot and how to process contextualized information and implement multi-round conversation functionality.
• summarize: Summarize the core knowledge points of the course through a mind map to help students systematically review and consolidate what they have learned.

Course address for ChatGPT Tip Engineering for Developers

• Course Address::DeepLearning.AI

Developer-oriented ChatGPT Prompt Project for People

• full-stack developer: Full-stack developers use ChatGPT to build chatbots, intelligent customer service, and other projects that integrate natural language processing to improve application interactivity.
• Backend Developer: Backend developers are responsible for optimizing ChatGPT's API call logic to ensure efficient and stable operation of the system and provide strong backend support for the application.
• front-end developer: Front-end developers design user interfaces that interact with ChatGPT, such as chat interfaces, to optimize the user experience and make interactions smoother and more natural.
• Natural Language Processing (NLP) Specialist: NLP experts perform tasks such as text generation and sentiment analysis with the help of ChatGPT, tapping the potential in the field of natural language processing and improving model performance.
• Machine Learning Engineer: Machine learning engineers combine ChatGPT with other models to build complex systems, such as intelligent recommender systems, expanding the boundaries of AI applications.