Course Outline
Day 1 | Grasping the Tools and Initial Build
Module 1 | How AI Coding Tools Operate
Covered topics:
• Understanding context windows and their constraints
• Statelessness and how AI models maintain information throughout a session
• The Plan → Execute → Review workflow
• Areas where AI coding tools excel versus those where they struggle
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Ecosystem
Covered topics:
• Overview of the current landscape of AI coding solutions
• Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Strengths and limitations of various coding assistants
• Practical strategies for integrating these tools into development teams
Module 3 | Anatomy of a Prompt
Covered topics:
• Essential components of an effective prompt
• Providing clear context and defining the task accurately
• 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
Covered topics:
• Constructing a project starting with an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining the generated code
• Testing and polishing the final solution
Day 2 | Existing Codebases, Personalization, and Review
Module 5 | Working Within a Codebase
Covered topics:
• Navigating and comprehending an unfamiliar codebase
• Querying and analyzing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating the onboarding process for existing projects
Module 6 | Daily Tasks: Debugging, Features, and Testing
Covered topics:
• 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 routine development activities
Module 7 | Personalization: Core Concepts
Covered topics:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Where and when personalization mechanisms are applied
• Best practices for configuring AI assistants
• Overview of advanced implementation approaches
Module 8 | Guardrails, Risks, and Judgment
Covered topics:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks can be delegated to AI
• Applying human judgment and maintaining accountability in software development
Requirements
No previous experience with coding or AI tools is necessary.
Familiarity with code or Git is beneficial.
A valid licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
Individuals new to AI-assisted development, including non-coders, occasional developers, and technical professionals in QA, data science, 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
