From tool proficiency to disciplined daily workflow -- master AI-assisted development with the methodology that makes it reliable.
This learning path presents a sequence of courses that build on each other, bundled to form a coherent program rather than a collection of isolated workshops.
Why This Path Exists
AI coding tools are powerful – but power without discipline is a liability. Most developers plateau after the initial excitement: they can generate code fast, but they can’t trust it. They spend as much time reviewing and fixing as they saved generating.
This path takes you from tool proficiency through the disciplines that make AI-assisted development reliable: test-driven development as your specification language, structured decomposition before generation, and finally the integrated workflow that weaves it all together.
Your tool of choice for the deep-dive module. Methodology that works with any AI coding assistant for the rest.
Your Progression
Spaced sessions, 1-2 per week. Practice on your actual codebase between modules. Spaced learning produces substantially better retention than compressed delivery.
Go deep with your AI coding tool of choice. Advanced prompting, workspace configuration, tool customisation -- the foundation for everything that follows.
Learning objectives
Two days of intensive work with your chosen AI coding tool. You go beyond basic prompting into advanced configuration, workspace setup, and the customisation that makes the tool fit your workflow.
- Advanced prompting techniques and context management
- Workspace and project configuration
- Tool customisation: commands, shortcuts, integration with your stack
- Code review patterns for AI-generated output
- Building reusable prompt libraries
Module 1 gave you depth. Module 2 gives you control. Tests become your specification language -- you define 'correct', AI implements, tests verify.
Learning objectives
You stop reacting to AI output and start directing it. Tests become your primary communication tool – you specify what “correct” means, AI generates implementations, and the tests verify the result.
- Tests as permanent, verifiable specifications (vs. ephemeral prompts)
- The red-green-refactor rhythm with AI as implementer
- AAA pattern (Arrange-Act-Assert) as specification structure
- Three-stage test development: ideas, outlines, implementations
- Recognising common AI bug patterns (happy path bias, success theatre)
- Context management: tests as persistent anchors across sessions
Where do the test cases come from? Decompose problems systematically before generating code. Each module adds a layer of control.
Learning objectives
You work at a level above implementation. Module 2 taught you to specify at the code level (tests); Module 3 teaches you to specify at the requirements and structure level. Together, they give you control at every level of abstraction.
- The spec-driven stack: requirements, plans, tasks, test specifications
- Writing acceptance criteria that constrain AI toward correct solutions
- Decomposing problems systematically before code generation
- Using AI itself for decomposition and critique
- The iceberg model: detail what’s immediate, sketch what’s next
- Deciding when to specify more vs. iterate faster
Weave everything together on realistic challenges -- including the messy, non-linear parts. Leave with a customised tool setup for your own projects.
Learning objectives
Three separate skills (tool use, TDD, SDD) become one integrated workflow. You practice it on realistic challenges – including the messy, non-linear parts – until it becomes natural. You leave with a customised tool setup that makes the workflow repeatable.
- Navigating non-linear development: detours, revisions, mid-flight changes
- Feedback loops: when test failures should update the spec
- Review patterns for AI-generated code: what to trust, what to verify
- Session management: starting, resuming, maintaining context
- Tool customisation: CLAUDE.md, skills, hooks that encode your workflow
- Knowing when to slow down: when AI confidence exceeds AI correctness
What's next
Design workflows where AI operates with significant autonomy under human oversight.
For those building AI into products, not just using AI to build products.
Price
3,635 EUR
Individual booking total:
4,660 EUR
Save 22%
Each module can also be booked individually -- see the course pages linked above for dates and pricing.
Booking Information
- All prices are exclusive of applicable taxes
- VAT will be added at checkout based on your location
- Direct checkout is only available for certain regions. Customers from other regions are welcome to book through email-based invoicing
- Secure payment processing
- Calendar invitations will be sent upon confirmation
- Cancellation Policy:
- Free cancellation up to 14 days before the first module
- 50% fee for cancellations 7-13 days before the first module
- No refund for cancellations less than 7 days before the first module
- Substitution of participants permitted at no extra charge
- We reserve the right to cancel trainings with unsustainably low bookings up to 7 days ahead of the first scheduled delivery
- Unless specified otherwise, trainings are conducted remotely. Participants are expected to have stable Internet connection and working microphone
Company-Internal Training
Need training for your team? We offer customized company-internal training sessions tailored to your specific requirements and schedule.
- Customized content for your team's needs
- Flexible scheduling at your convenience
- On-site or remote delivery options
- Volume discounts for larger groups
- Pay by invoice
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