Get in Touch
 Duration 14 hours

Course Outline

Day 1 Outline

Module 1 — Introduction to Claude Code & AI-Driven Engineering

• Claude Code versus traditional AI tools
• AI agents in software engineering
• Productivity and workflow optimization
• AI-assisted development lifecycle
• Risks, limitations, and human oversight
• Live practical demonstrations

Module 2 — Fundamentals of Prompt Engineering

• Structure of an effective prompt
• Zero-shot versus few-shot prompting
• Iterative prompting techniques
• Basics of prompt chaining
• Structured outputs and formatting
• Prompt validation and quality enhancement

Module 3 — Prompting for Software Development

• Code generation and refactoring
• Debugging with AI support
• Documentation creation
• Pull request reviews
• Understanding legacy code
• Safe and maintainable AI-generated code

Module 4 — Prompting for Testing & Quality Assurance

• Test case generation
• Edge-case analysis
• Automation-ready test design
• AI-assisted defect analysis
• Creating Gherkin and test scenarios
• Quality verification workflows

Module 5 — Prompting for Agile Collaboration

• User stories and acceptance criteria
• Requirements refinement
• Agile communication support
• Stakeholder summaries
• Retrospective assistance
• Preparing for backlog refinement

Module 6 — Responsible AI, Security & Validation

• Hallucinations and AI risks
• Confidentiality and secure prompting
• AI governance principles
• Verification checklists
• Awareness of prompt injection
• Human review responsibilities

Module 7 — Team Prompt Laboratory

• Creating reusable team prompts
• Role-specific AI workflows
• Prompt sharing and peer review
• Creating Team Prompt Library v1
• Interactive collaborative exercises

Day 2

Module 1 — Advanced Capabilities of Claude Code

• CLAUDE.md and persistent project context
• AI workflow automation
• Best-of-N generation strategies
• Reusable AI commands
• Context engineering techniques
• AI-assisted engineering workflows

Module 2 — Advanced Prompt Engineering Techniques

• Chain-of-thought prompting
• Multimodal prompting
• Constraint-based prompting
• Advanced prompt chaining
• Managing large contexts
• Conversational engineering workflows

Module 3 — Version Control, Parallel Development & Multi-Agent Workflows

• Git integration strategies
• Parallel AI development workflows
• Worktrees and isolated AI tasks
• Multi-agent orchestration
• Human-in-the-loop checkpoints
• Conflict management strategies

Module 4 — Architecture, MCP & Advanced DevOps

• Model Context Protocol (MCP)
• Claude integrations with external tools
• AI-assisted architecture analysis
• Architecture Decision Records (ADR)
• AI-assisted CI/CD troubleshooting
• Incident postmortems and operational workflows

Module 5 — Scaling Claude Code & Codebase Health

• Token and context management
• AI-friendly project structures
• Long-term codebase maintainability
• Documentation automation
• AI scalability strategies
• Team-wide engineering workflows

Module 6 — Capstone: Defining Your Claude Code Process

• Designing scalable AI-assisted workflows
• Combining prompts, commands, and context files
• Designing team AI processes
• Cross-role collaboration models
• Creating workflow blueprints

Module 7 — Advanced Team Prompt Laboratory

• Developing advanced prompt libraries
• Complex role-specific workflows
• Real-world prompt validation
• Cross-team collaboration exercises
• Team Prompt Library v2

Requirements

Day 1 — Foundations

• Basic knowledge of 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 experience)
• Prior exposure to Claude Code and prompt engineering concepts
• Fundamental Git knowledge
• Familiarity with CI/CD concepts is recommended

Number of participants


Price per participant

Testimonials (3)

Upcoming Courses

Related Categories