Course Outline
Day 1 | Foundational Principles and Initial Implementation
Module 1 | Operational Mechanics of AI Coding Utilities
Curriculum Overview:
• Comprehension of context window parameters and associated constraints
• Session state management and information retention mechanisms in AI models
• The Plan → Execute → Review operational workflow
• Capabilities and technical limitations of automated coding assistants
• Established practices for effective human-AI collaboration
Module 2 | Ecosystem Analysis of AI Coding Solutions
Curriculum Overview:
• Comprehensive survey of the current AI coding landscape
• Comparative analysis of primary platforms, including Cursor, GitHub Copilot, and Claude Code
• Criteria for selecting appropriate models and tools based on specific operational requirements
• Evaluation of strengths and limitations across various coding assistants
• Strategic guidelines for integrating these technologies into development teams
Module 3 | Structure of Effective Prompts
Curriculum Overview:
• Essential components required for high-quality prompts
• Methods for establishing clear context and defining task objectives
• Specifications for output formats and operational constraints
• Standardized prompting frameworks and templates
• Techniques for enhancing consistency and accuracy in prompt engineering
Module 4 | Initial Development: Construction from Base State
Curriculum Overview:
• Project initialization within a directory structure
• Establishment of initial application architecture and scaffolding
• Management of project dependencies and configuration files
• Iterative refinement of generated code outputs
• Validation procedures for the final solution
Day 2 | Codebase Integration, Customization, and Oversight
Module 5 | Operations Within Established Codebases
Curriculum Overview:
• Navigation and comprehension of unfamiliar code structures
• Utilization of AI tools for querying and analyzing existing projects
• Mapping application architecture and dependency chains
• Generation of technical documentation and executive summaries
• Acceleration of personnel onboarding into active development cycles
Module 6 | Routine Development Tasks: Remediation, Enhancement, and Testing
Curriculum Overview:
• Application of AI tools for bug identification and resolution
• Implementation of new features and system enhancements
• Authoring and optimizing automated test suites
• Validation of generated code modifications
• Optimization of productivity in daily software development activities
Module 7 | Customization: Conceptual Framework
Curriculum Overview:
• Review of project-specific rules and configuration parameters
• Introduction to AGENTS.md standards and persistent memory protocols
• Operational scope of personalization mechanisms
• Best practices for configuring AI assistant behaviors
• Overview of advanced implementation strategies
Module 8 | Safeguards, Risk Assessment, and Professional Judgment
Curriculum Overview:
• Review and validation protocols for AI-generated code
• Identification of common failure modes and technical limitations
• Recognition of prompt injection vulnerabilities and security implications
• Criteria for determining tasks suitable for automation
• Application of professional judgment and maintenance of accountability in software engineering for government
Requirements
Testimonials (2)
Using Claude Code in a more efficient way
Virgil Trif - Frequentis
Course - Claude Code: Agentic AI Development · 1-Day
"I learned the potential of the tool and gained sufficient skills to start using it for my work right away