Generative AI Tools for Technologists
Generative AI has transformed the development landscape. We have seen clients greatly improve developer productivity through training staff on how to use Gen AI tools effectively from within their IDEs. Through this course, developers will learn how to use tools such as GitHub CoPilot, Amazon Q and SourceGraph Cody to increase productivity, reduce errors, and reduce time to market. Specifically, in this course you will learn how to use these tools to fix bugs, upgrade applications, create applications, create unit tests, explain code and much more.
Prerequisites
- This course will be most beneficial to those with development or scripting experience
Contents
Generative AI and the SDLC
- Current tool landscape
- GitHub CoPilot
- SourceGraph Cody
- Amazon Q
- Integrating Tools into the IDEs
Setting the Scene
- Initial Prompts - your skill level
- Initial Prompts - the quality of the code you require
- Initial Prompts - pointing to existing coding standards and styles
Explaining Code
- Setting your skill level
- Ensuring the context files are available to the LLM
- Effective prompting for explanations
Debugging
- Ensuring the context
- Critically Evaluating suggested solutions
- Accepting solutions that affect multiple files
Upgrading Applications
- A sample flow for how to upgrade an application
- Avoid doing too much at once
- Asking the right questions when things don't work
Migrating Legacy Code
- Migrating Legacy Code to modern architectural styles and platforms
- Approach 1: Document code and then create a new app from the documentation
- Approach 2: Direct conversions
- Ensuring high standards
Testing
- Generating effective tests
- Validating tests
- Refectoring as you go
Pitfalls and Safeguards
- Knowing the limits of Generative AI
- Who's responsible for the Code
- The potential future landscape of Generative AI in software development
Worked Examples
- Completing a series of challenges using GenAI Tools
- Creating an application using GenAI Tools
