Build with AI: Beyond Pair Programming with AI

Course details

Effective use of AI coding assistants can speed up and improve the software development process through rapid code generation, iteration, experimentation, and a modern take on test-driven development (TDD). Apply AI tools to build and expand coding projects, create new components by generating fit-for-purpose boilerplate code, validate projects with AI-generated tests, automatically document and expand projects, and more. The course covers building new projects from scratch as well as using AI to upgrade and refactor existing codebases.

Learning objectives
Evaluate the capabilities of various AI coding assistants and integrate them into the software development workflow to enhance productivity and code quality.
Design and implement code components, leveraging AI tools to generate code templates, expand functionality, and build new features from developer instructions.
Analyze existing codebases, using AI-driven refactoring techniques to optimize code structure, update legacy code to modern standards, and improve overall maintainability.
Develop test cases and apply test-driven development (TDD) methodologies with AI assistants, generating comprehensive tests and validating code functionality to ensure reliability.
Iterate on different coding approaches and solutions, using AI tools to explore alternative implementations, refine code, and innovate beyond conventional programming paradigms.

Concepts

0. Introduction

01 - Pair-programming with AI
02 - Prerequisites and how to use the exercise files

1. Tools, Materials, and Environments

03 - Tools, materials, and environments
04 - AI as a chatty collaborator
05 - AI in the IDE
06 - AI in the pilot seat
07 - AI and rethinking what a developer is

2. Looking the AI in the Mouth

08 - Where to start and how to start
09 - Inline generation
10 - Seeding, selecting, and expanding
11 - Generating documentation
12 - Documentation-Driven Development (DDD )
13 - Chatting with code
14 - Code from a chat (ChatGPT)
15 - Artifacts (Claude)
16 - Knowledge, trust, and conservative models

3. Iteration, Experimentation, and Expansion

17 - Let's build an app together
18 - Start with a prompt
19 - Use AI to build the layout
20 - Add an interactive map
21 - Use DDD to add an AI chat
22 - AI-driven code iteration
23 - Hallucinations, errors, and how to address them

4. Tests and Test-Driven Development

24 - AI and the renewed importance of tests
25 - Writing tests with AI
26 - Getting AI to set up your test environment
27 - Effective TDD with modern AI tools
28 - The challenges of AI-powered TDD

5. Pushing Forward with Conservative Tools

29 - Understanding bias in LLMs
30 - Detecting amplified old patterns
31 - Coaching the AI to use modern standards
32 - Documentation-driven AI assistance
33 - Leveraging pattern matching to push forward

6. Conclusion

34 - Upskilling is the new skill