Course Outline
Day 1 | Foundational Tools and Initial Development
Module 1 | Operational Mechanics of AI Coding Tools
Topics covered:
• Context window capacity and associated operational constraints
• Stateless architecture and information retention protocols within sessions
• The Plan, Execute, and Review operational workflow
• Identification of high-performing capabilities versus known limitations
• Best practices for effective human-AI collaboration in government environments
Module 2 | Current AI Coding Ecosystem
Topics covered:
• Comprehensive overview of the prevailing AI coding infrastructure
• Differentiating features among tools such as Cursor, GitHub Copilot, and Claude Code
• Criteria for selecting appropriate models and tools for specific government tasks
• Assessment of strengths and constraints across various coding assistants
• Strategic recommendations for tool integration within development teams
Module 3 | Prompt Structure and Design
Topics covered:
• Essential elements of a high-efficacy prompt
• Contextual provision and precise task definition
• Specification of output formats and operational constraints
• Application of standard prompting frameworks and templates
• Methods for enhancing prompt consistency and quality standards
Module 3 | Initial Implementation: Development from Ground Up
Topics covered:
• Initiating project development from a blank repository
• Establishing foundational application architecture and scaffolding
• Management of dependencies and project configuration standards
• Iterative refinement of generated code artifacts
• Testing and validation of the final solution
Day 2 | Integration with Existing Codebases, Customization, and Oversight
Module 5 | Interaction with Existing Codebases
Topics covered:
• Navigation and comprehension of unfamiliar code structures
• Utilization of AI tools for querying and analyzing established projects
• Mapping of application architecture and dependency relationships
• Generation of technical documentation and summary reports
• Acceleration of onboarding processes for legacy systems
Module 6 | Routine Operations: Correction, Enhancement, and Testing
Topics covered:
• Leveraging AI tools for defect investigation and resolution
• Implementation of new features and functional enhancements
• Development and optimization of automated testing suites
• Verification of generated code and proposed changes
• Enhancement of productivity in routine development activities for government projects
Module 7 | Customization: Conceptual Framework
Topics covered:
• Interpretation of project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identification of applicable personalization mechanisms
• Best practices for configuring AI assistants within secure environments
• Overview of advanced implementation methodologies
Module 8 | Controls, Risk Management, and Professional Judgment
Topics covered:
• Rigorous review and validation of AI-generated code
• Understanding common failure modes and system limitations
• Recognition of prompt injection vectors and security threats
• Determination of tasks suitable for AI delegation
• Application of human judgment and maintenance of accountability in software development for government
Requirements
No prior coding or AI tool experience is mandatory.
Basic proficiency with code or Git is recommended.
Licensed account access (Claude Code / Cursor / Copilot).
Audience:
Individuals new to AI-assisted development, including non-coders, occasional developers, and technical-adjacent roles in QA, data, product, or operations. No prior development background is assumed.
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
