Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hours
Cost: $1,495 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

This prospectus defines a three-session, 14-contact-hour training engagement for engineering teams already using or evaluating Claude Code or comparable AI-assisted coding tools. The program moves beyond introductory feature awareness into disciplined, repeatable engineering workflows that improve productivity while preserving quality, security, and human oversight. 

The training is positioned as an applied engineering enablement program, not a basic Claude Code orientation. Participants will practice concrete workflows and leave with shared understanding for when and how to use Claude Code responsibly in an enterprise SDLC.

The core program focuses on Claude Code for engineering productivity. Related capabilities such as Claude Code GitHub Actions, CI/CD automation, Claude Cowork, Remote Control, and enterprise administration are presented as optional enterprise modules that can be included based on client access, policy, and audience needs.

In this course you will learn

• Understand the fit of Claude Code into modern development workflows and enterprise SDLC practices.
• Understand the changing role of the developer with respect to terminal-native and agentic AI tools.
• Grow confidence in managing Claude Code workflows safely and productively.
• Understand AI tradeoffs, challenges, and situations where Claude Code should not be used.
• Use Plan Mode and explicit constraints to guide implementation before files are changed.
• Write prompts that include scope, context, constraints, examples, validation steps, and acceptance criteria.
• Use Claude Code to understand unfamiliar repositories and generate onboarding documentation.
• Generate and improve tests, identify edge cases, and validate AI-assisted changes.
• Review diffs, commands, dependency changes, and generated code before acceptance.
• Configure project guidance with CLAUDE.md and understand user, project, local, and managed settings.
• Understand where skills, subagents, hooks, MCP servers, plugins, and slash commands fit.
• Optional: Understand Claude Cowork, Remote Control, and related features if desired.
  

Core Claude Code Feature Areas Reflected

• Claude Code CLI workflows
• Claude Code on the web where enabled by plan and policy
• Plan Mode and implementation workflows
• Permission modes, command approvals, trusted repositories, and safe execution practices
• Repository and team guidance through CLAUDE.md
• User, project, local, and managed settings for enterprise governance
• Custom slash commands and reusable workflows
• Skills using SKILL.md for reusable task knowledge
• Subagents for specialized roles and parallelized work patterns
• Hooks for deterministic checks and workflow enforcement
• MCP servers and remote MCP for approved tool and data-source access
  
  

Expected Deliverables

• Slide deck and/or slide PDFs
• Step-by-step hands-on labs
• Prompt pattern handout
• AI-assisted coding guardrails checklist
• Team working agreement template

What you will need

• Claude Code access approved by the client organization
• Basic command-line familiarity
• Software development experience
• Access to a preconfigured lab repository or Codespace/local environment
• Ability to attend all sessions
  

This course is great for

• Software engineers actively using or evaluating Claude Code
• Technical leads who need strategic approaches for AI-assisted development
• Teams seeking practical improvements in planning, implementation, refactoring, tests, debugging, documentation, and review workflows
• Platform, DevOps, or developer productivity teams evaluating governed agentic engineering workflows
  

Topics Covered

Improve Productivity -  Practice Claude Code workflows for accelerating planning, repository understanding, implementation, test creation, refactoring, debugging, documentation, and validation while maintaining developer control.   

Prepare for future engineering roles -  Introduce agentic workflows, Plan Mode, MCP/tool integration, skills, subagents, hooks, managed settings, and optional agent teams. Explain how these capabilities map to current and emerging AI-assisted engineering roles.

Build hands-on experience -  Use guided labs with realistic engineering tasks. Labs emphasize how to use Claude Code safely and effectively rather than turning the course into generic coding exercises. Each lab is designed for 10-12 steps and approximately 10-12 minutes.

Increase confidence -  Develop comfort managing AI as a tool: scoping requests, planning first, reviewing diffs, approving commands, recovering from failures, and validating outputs with tests and human review.

Apply tools -  Use issue-driven workflows, repository onboarding, test generation, debugging, refactoring, documentation, and review activities that mirror real SDLC work.

Improve quality and consistency -  Create a team playbook, guardrails checklist, starter customization files, and a working agreement for AI-assisted coding.