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AI-Assisted Programming for Web and Machine Learning

You're reading from   AI-Assisted Programming for Web and Machine Learning Improve your development workflow with ChatGPT and GitHub Copilot

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835086056
Length 602 pages
Edition 1st Edition
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Authors (5):
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Marina Fernandez Marina Fernandez
Author Profile Icon Marina Fernandez
Marina Fernandez
Ajit Jaokar Ajit Jaokar
Author Profile Icon Ajit Jaokar
Ajit Jaokar
Anjali Jain Anjali Jain
Author Profile Icon Anjali Jain
Anjali Jain
Christoffer Noring Christoffer Noring
Author Profile Icon Christoffer Noring
Christoffer Noring
Ayşe Mutlu Ayşe Mutlu
Author Profile Icon Ayşe Mutlu
Ayşe Mutlu
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Toc

Table of Contents (25) Chapters Close

Preface 1. It’s a New World, One with AI Assistants, and You’re Invited FREE CHAPTER 2. Prompt Strategy 3. Tools of the Trade: Introducing Our AI Assistants 4. Build the Appearance of Our App with HTML and Copilot 5. Style the App with CSS and Copilot 6. Add Behavior with JavaScript 7. Support Multiple Viewports Using Responsive Web Layouts 8. Build a Backend with Web APIs 9. Augment Web Apps with AI Services 10. Maintaining Existing Codebases 11. Data Exploration with ChatGPT 12. Building a Classification Model with ChatGPT 13. Building a Regression Model for Customer Spend with ChatGPT 14. Building an MLP Model for Fashion-MNIST with ChatGPT 15. Building a CNN Model for CIFAR-10 with ChatGPT 16. Unsupervised Learning: Clustering and PCA 17. Machine Learning with Copilot 18. Regression with Copilot Chat 19. Regression with Copilot Suggestions 20. Increasing Efficiency with GitHub Copilot 21. Agents in Software Development 22. Conclusion 23. Other Books You May Enjoy
24. Index

Prompting strategy

Our prompting strategy is like before, a blended approach of using the in-editor experience and adding prompts in open text files to bring up the Chat window in Copilot; mix these approaches to your discretion.

As for prompts, there should be enough context in these prompts to make Copilot aware that it will need to suggest a design for specific devices. Thus, it should be able to infer from context what resolutions, pixel density, and other details should influence the suggestions it’s about to generate. As for prompting pattern used, we will use the “Exploratory prompt pattern” described in Chapter 2.

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