Course Outline
Day 1 Curriculum
Module 1 — Overview of Claude Code and AI-Enhanced Engineering
• Comparative analysis of Claude Code against conventional AI utilities
• Application of AI agents in software engineering disciplines
• Optimization of productivity and operational workflows
• Integration of AI assistance across the development lifecycle
• Identification of risks, limitations, and human oversight requirements
• Live technical demonstrations
Module 2 — Core Prompt Engineering Principles
• Structure of effective prompts
• Zero-shot versus few-shot prompting methodologies
• Iterative prompting strategies
• Fundamentals of prompt chaining
• Standardization of output formats and structure
• Validation and quality enhancement of prompts
Module 3 — Prompt Engineering for Software Development
• Code generation and refactoring processes
• AI-assisted debugging techniques
• Automated documentation creation
• Review processes for pull requests
• Interpretation of legacy codebases
• Ensuring safety and maintainability in AI-generated code
Module 4 — Prompt Engineering for Testing and Quality Assurance
• Generation of test cases
• Analysis of edge cases
• Design of tests suitable for automation
• AI-assisted defect analysis
• Creation of Gherkin and test scenarios
• Quality verification protocols
Module 5 — Prompt Engineering for Agile Collaboration
• Formulation of user stories and acceptance criteria
• Refinement of requirements
• Support for agile communication standards
• Preparation of stakeholder summaries
• Assistance in retrospective processes
• Preparation for backlog refinement
Module 6 — Responsible AI, Security, and Verification
• Management of hallucinations and associated AI risks
• Maintenance of confidentiality and secure prompting practices
• Principles of AI governance
• Implementation of verification checklists
• Awareness of prompt injection threats
• Definition of human review responsibilities
Module 7 — Team Prompt Laboratory
• Development of reusable team prompts
• Establishment of role-specific AI workflows
• Peer review and sharing of prompts
• Creation of Team Prompt Library v1
• Interactive collaborative exercises
Day 2
Module 1 — Advanced Capabilities of Claude Code
• Utilization of CLAUDE.md for persistent project context
• Automation of AI workflows
• Best-of-N generation strategies
• Implementation of reusable AI commands
• Context engineering methods
• AI-assisted engineering processes
Module 2 — Advanced Prompt Engineering Techniques
• Chain-of-thought prompting methodologies
• Multimodal prompting approaches
• Constraint-based prompting strategies
• Advanced prompt chaining procedures
• Management of large context windows
• Conversational engineering workflows
Module 3 — Version Control, Parallel Development, and Multi-Agent Workflows
• Git integration strategies
• Parallel AI development processes
• Use of worktrees and isolated AI tasks
• Orchestration of multi-agent systems
• Implementation of human-in-the-loop checkpoints
• Conflict management strategies
Module 4 — Architecture, MCP, and Advanced DevOps
• Model Context Protocol (MCP) implementation
• Integration of Claude with external tools
• AI-assisted architectural analysis
• Creation of Architecture Decision Records (ADR)
• AI-assisted troubleshooting of CI/CD pipelines
• Incident postmortems and operational workflows
Module 5 — Scaling Claude Code and Codebase Health
• Management of tokens and context
• Development of AI-compatible project structures
• Ensuring long-term codebase maintainability
• Automation of documentation processes
• Strategies for AI scalability
• Organization-wide engineering workflows
Module 6 — Capstone: Defining Your Claude Code Process
• Design of scalable AI-assisted workflows
• Integration of prompts, commands, and context files
• Design of team AI processes
• Models for cross-role collaboration
• Creation of workflow blueprints
Module 7 — Advanced Team Prompt Laboratory
• Development of advanced prompt libraries
• Implementation of complex role-specific workflows
• Validation of prompts in real-world scenarios
• Cross-team collaboration exercises
• Finalization of Team Prompt Library v2
Requirements
Day 1 — Foundation
• Basic familiarity with software delivery processes
• General understanding of development, testing, or agile workflows
• Access to Claude is recommended for hands-on exercises
Day 2 — Advanced
• Completion of Day 1 or equivalent professional experience
• Prior exposure to Claude Code and prompt engineering concepts
• Basic Git knowledge
• Familiarity with CI/CD concepts is recommended
Testimonials (2)
Considerations about privacy and policy
Angelo Stefano Soglia - Parkino srl
Course - Claude AI for Workflow Automation and Productivity
Learning how to prompt Claude and use it to digest all of the data I have available.