1 Introduction to Generative AI

 

This chapter covers:

  • How generative AI transforms coding with context-aware help
  • The evolution of AI dev tools from IDE integration to standalone assistance
  • LLM fundamentals and code generation capabilitiesAI enhanced workflows from idea to deployment
  • Success factors for integrating AI into your development process
Robots are not going to replace humans, they are going to make their jobs much more humane. Difficult, demeaning, demanding, dangerous, dull – these are the jobs robots will be taking.

- Sabine Hauert, Co-founder of Robohub.org

What if you could leverage your existing Python expertise alongside AI that understands your code context, anticipates patterns, and generates implementation details while you focus on architecture and design? That's the power of generative AI tools for experienced developers. When I first encountered these tools, I approached them with healthy skepticism. But after integrating them into real production projects over the past year, I've reduced implementation time by approximately 30% while improving code quality and test coverage.

1.1 Generative AI for coders

1.1.1 Code generation and autocompletion

1.1.2 Bug detection and automated fixes

1.1.3 Documentation generation

1.1.4 Code refactoring and optimization

1.1.5 Test case generation and mock data creation

1.2 Developer tools landscape

1.2.1 Integrated developer tools

1.2.2 Standalone tools

1.3 How does Generative AI work?

1.4 What is an LLM, and why should I care?

1.5 Why do these tools sometimes get it wrong?

1.5.1 How LLMs differ from databases

1.5.2 Training phase issues

1.5.3 Misinterpreting context

1.6 The potential of LLMs

1.7 Generative AI vs. code completion

1.7.1 Other types of generative AI

1.7.2 Why coders care about generative AI

1.8 Project workflow with AI assistance

1.8.1 Ideation and Planning

1.8.2 Code Generation and Assistance

1.8.3 Code Review and Analysis

1.8.4 Testing and Debugging

1.8.5 Documentation and Content Generation

1.9 Choosing the right Generative AI tools

1.9.1 Data Quality and Availability

1.9.2 Integration with Development Workflows

1.9.3 Quality Assurance

1.9.4 Keeping up with evolving tools

1.9.5 Shift in focus

1.10 Don’t fear the rise of AI

1.11 Go forth and code!

1.12 Summary