AI Developer Learning Path

Learn how to use an AI assistant for explaining technical concepts, reviewing code, generating examples, and planning development tasks.

Use a second AI assistant to compare explanations, code reviews, and long-context reasoning instead of relying on a single model.

Build a broad understanding of artificial intelligence, its capabilities, limitations, terminology, and practical role before moving into AI application development.

Confirm that you understand prompts, model limitations, hallucinations, context, and the importance of validating generated output.

Learn practical prompt engineering techniques for software development and LLM-powered applications before applying them inside an AI-assisted coding workflow.

Practice AI-assisted coding, codebase questions, inline editing, debugging, and refactoring inside an AI-enabled development environment.

Compare code completion and development assistance workflows with another established AI coding tool.

Study how multiple LLM calls can be combined into practical application workflows, providing a foundation for building a structured AI-powered prototype.

Create a small application that accepts user input, sends a structured prompt to an AI service, and displays the generated result.

Study how AI-assisted application builders can accelerate prototyping while still requiring careful review of generated code and architecture.

Improve the prototype by validating inputs and outputs, handling API failures, and preventing invalid generated data from reaching the interface.

Build a complete AI-powered application that solves a clearly defined user problem and demonstrates responsible development practices.

Review the completed portfolio project and confirm that the essential technical and documentation requirements have been met.