English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8h 36m | 1.15 GB
Practical techniques to accelerate software development using generative AI.
Let’s get real. You’d like to hand off a lot of tedious software development tasks to an assistant—and now you can! AI-powered coding tools like Copilot can accelerate research, design, code creation, testing, troubleshooting, documentation, refactoring and more. Coding with AI shows you how. Written for working developers, this book fast-tracks you to AI-powered productivity with bite-size projects, tested prompts, and techniques for getting the most out of AI.
In Coding with AI you’ll learn how to:
- Incorporate AI tools into your development workflow
- Create pro-quality documentation and tests
- Debug and refactor software efficiently
- Create and organize reusable prompts
Coding with AI takes you through several small Python projects with the help of AI tools, showing you exactly how to use AI to create and refine real software. This book skips the baby steps and goes straight to the techniques you’ll use on the job, every day. You’ll learn to sidestep AI inefficiencies like hallucination and identify the places where AI can save you the most time and effort.
Taking a systematic approach to coding with Al will deliver the clarity, consistency, and scalability you need for production-grade applications. With practice, you can use AI tools to break down complex problems, generate maintainable code, enhance your models, and streamline debugging, testing, and collaboration. As you learn to work with AI’s strengths—and recognize its limitations—you’ll build more reliable software and find that the quality of your generated code improves significantly.
Coding with AI shows you how to gain massive benefits from a powerful array of AI-driven development tools and techniques. And it shares the insights and methods you need to use them effectively in professional projects. Following realistic examples, you’ll learn AI coding for database integration, designing a UI, and establishing an automated testing suite. You’ll even vibe code a game—but only after you’ve built a rock-solid foundation.
What’s Inside
- Incorporate AI into your development workflow
- Create pro-quality documentation and tests
- Debug and refactor software efficiently
- Create and organize reusable prompts
Table of Contents
1 Part 1. Getting started with AI-assisted coding
2 Chapter 1. Introducing generative AI
3 Chapter 1. Developer tools landscape
4 Chapter 1. How does generative AI work
5 Chapter 1. What is an LLM, and why should I care
6 Chapter 1. Why do these tools sometimes get it wrong
7 Chapter 1. The potential of LLMs
8 Chapter 1. Generative AI vs. code completion
9 Chapter 1. Project workflow with AI assistance
10 Chapter 1. Choosing the right generative AI tools
11 Chapter 1. Don t fear the rise of AI
12 Chapter 1. Go forth and code!
13 Chapter 1. Summary
14 Chapter 2. First steps with AI-assisted coding
15 Chapter 2. Common patterns
16 Chapter 2. Context is everything
17 Chapter 2. What is NLP
18 Chapter 2. A simple Python project
19 Chapter 2. Summary
20 Part 2. Building applications with AI assistance
21 Chapter 3. Design and discovery
22 Chapter 3. The problem
23 Chapter 3. Creating the right prompt
24 Chapter 3. Measuring the effect on the design process
25 Chapter 3. A design document created with ChatGPT
26 Chapter 3. Software design document – HAM radio license practice test application
27 Chapter 3. Digging deeper
28 Chapter 3. Generating user stories for our project
29 Chapter 3. Summary
30 Chapter 4. Coding the first version of our application
31 Chapter 4. Extracting requirements from the design
32 Chapter 4. Setting up our development environment
33 Chapter 4. Flask application structure – Output from ChatGPT
34 Chapter 4. Stubbing out our application
35 Chapter 4. Running our application
36 Chapter 4. Summary
37 Chapter 5. Using Blackbox AI to generate base code
38 Chapter 5. Setting up the development environment
39 Chapter 5. Developing core features
40 Chapter 5. Summary
41 Chapter 6. Generating a software backend with Tabnine
42 Chapter 6. Creating an index page
43 Chapter 6. Summary
44 Part 3. Advanced AI development techniques
45 Chapter 7. Building user interfaces with ChatGPT
46 Chapter 7. Creating our templates
47 Chapter 7. Describing the flow of our application
48 Chapter 7. Summary
49 Chapter 8. Building effective tests with generative AI
50 Chapter 8. What are unit tests
51 Chapter 8. The tools we ll use for Python testing
52 Chapter 8. Writing unit tests with generative AI
53 Chapter 8. Summary
54 Chapter 9. Prompt engineering
55 Chapter 9. Understanding the anatomy of a prompt
56 Chapter 9. Crafting the ultimate prompt
57 Chapter 9. Fundamental prompt types
58 Chapter 9. Advanced prompt types
59 Chapter 9. Prompt techniques for programmers
60 Chapter 9. Summary
61 Chapter 10. Vibe coding with Cursor
62 Chapter 10. What is Cursor, and why is it different
63 Chapter 10. First concept
64 Chapter 10. The initial prompt to build our game
65 Chapter 10. Cursor basics
66 Chapter 10. Results from the first prompt
67 Chapter 10. Running our game for the first time
68 Chapter 10. Making changes to our game
69 Chapter 10. Summary
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