Course Outline
Day 1 | Understanding the Tools and Initial Implementation
Module 1 | How AI Coding Tools Function
Coverage:
• Comprehending context windows and their constraints
• The nature of statelessness and how AI models retain information within a session
• The Plan → Execute → Review workflow
• Capabilities and limitations of AI coding tools
• Best practices for effective collaboration with AI assistants
Module 2 | The Landscape of AI Coding Solutions
Coverage:
• Overview of the current AI coding ecosystem
• Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Strengths and limitations of various coding assistants
• Practical advice on adopting these tools within development teams
Module 3 | Components of Effective Prompts
Coverage:
• Essential elements of a successful prompt
• Providing clear context and defining the task precisely
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing the quality and consistency of prompts
Module 4 | Initial Coding: Building from Scratch
Coverage:
• Developing a project starting with an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and polishing the final solution
Day 2 | Working with Existing Codebases, Personalization, and Review
Module 5 | Navigating a Codebase
Coverage:
• Exploring and understanding unfamiliar code structures
• Querying and analyzing existing projects using AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into ongoing projects
Module 6 | Daily Tasks: Bug Fixes, Features, and Testing
Coverage:
• Using AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and modifications
• Boosting productivity in everyday development activities
Module 7 | Personalization: Core Concepts
Coverage:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory mechanisms
• Applicable scenarios for personalization settings
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies
Module 8 | Guardrails, Risks, and Professional Judgment
Coverage:
• Reviewing and validating code produced by AI
• Understanding common failure modes and limitations
• Identifying prompt injection and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
There are no requirements for prior coding or AI-tool experience.
Familiarity with code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
The course is intended for those new to AI-assisted development, including non-coders, occasional programmers, and technical roles in QA, data, product management, or operations. No prior development background is expected.
Testimonials (2)
Learning how to prompt Claude and use it to digest all of the data I have available.
Mike Hartleroad - Furniture Row
Course - Claude AI for Data Analysis and Business Intelligence
how to engage with the Office environment and set up repetitive tasks
