What Participants Say
Overview
Many teams are experimenting with AI coding tools, but few achieve consistent, reliable results, especially in professional, complex codebases. Some developers report great output, others see results break down under industrial-scale complexity and strict quality requirements. The consequence: success is unpredictable and often depends on ad-hoc prompting habits rather than a shared, reliable process.
This training helps teams turn experimentation into a professional, predictable engineering practice. You will learn a clear, repeatable workflow for delegating tasks to AI, reviewing results, and iterating efficiently — without losing control over quality or maintainability.
The training is online and easy to scale: use it for individual developers or roll it out consistently across whole teams.
→ See how your team can get started
Why Structure Matters
Most organizations still rely on vibe coding — improvised prompting, discovery by accident, and inconsistent results. This works in small demos but breaks down in:
- long-lived and complex codebases
- challenging requirements and complex functionality
- non-mainstream architectures and technology stacks
- high-quality or safety-critical domains
- unpredictable results across developers
- difficult onboarding of new team members
- slow iterations and frequent dead ends
- misalignment on how to collaborate with AI tools
What Your Team Will Learn
The training teaches a step-by-step workflow for AI-assisted development that works across all major AI tools. It also builds a fundamental, tool-agnostic understanding of how AI coding agents operate — enabling your team to apply the method effectively in any current or future AI coding environment.Your team learns how to:
- Decide which tasks to delegate and how to scope them
- Define tasks clearly through Task Engineering and Context Engineering
- Invoke AI agents effectively and reuse context fragments across iterations
- Review & Decide on the next step from a predictable set of options
- Follow up with targeted refinements until a high-quality result is reached
- High success rate in daily AI coding tasks
- Faster iterations with more confidence in result quality
- Shared vocabulary and workflow across the team
- A culture of continuously adapting and specializing the AI workflow
- Predictable AI-assisted development in real-world projects
How Your Team Can Get Started
Organizations can adopt the training in two ways. Both options include access to the online course, and you can switch or combine them at any time.
Option A: Course-only (self-paced team training)
Your team receives access to the complete online training and integrates the workflow independently into your projects. This works well for smaller teams, pilot groups, or as a low-overhead starting point.Course seats are licensed individually at €199 per person.You can purchase seats directly online or request an invoice for B2B purchases.For group discounts or invoice-based purchases, please contact us.Purchase course seats for your teamOption B: Guided adoption package (workshops + hands-on support)
Teams that prefer supported adoption can add a flexible workshop package on top of the course. All participants still complete the online course, and we additionally support you with a tailored set of services. Typical components include:- Leadership briefing (optional)
- Environment & tooling enablement workshop
- Hybrid training day with live Q&A sessions
- Custom demo using your own project/codebase
- Follow-up Q&A after two weeks of real usage
Who It Is For
The training is designed for:
- Professional developers working with real-world, long-lived, or complex codebases
- Tech leads who want a shared, predictable workflow for AI-assisted development
- Teams adopting AI coding across multiple tools and projects
- Organizations modernizing their engineering practices with structured AI support
Methodology
The training is based on a structured AI coding workflow referred to as Dibe Coding. It is the result of years of hands-on experience across multiple AI coding tools, LLM generations, and real-world project iterations — shaped through continuous adaptation to new trends, capabilities, and engineering challenges. The approach is developer-first, tool-agnostic, and designed for real-world engineering work rather than prompt tricks or toy examples.The methodology covers:
- Task decomposition and scoping
- Task Engineering and Context Engineering
- Effective delegation to AI coding agents
- Predictable iteration loops
- Quality control and safe integration
Contact
Want to explore which option fits your team best? We are happy to discuss your setup, challenges, and goals — and help you choose the right approach for your team.Get in touchTell us your team size and goals and we’ll get back to you quickly and suggest next steps. Prefer to talk it through? Let’s schedule a call.Or email us directly at [email protected].
Related Articles & Resources
- AI Coding Training Now Available: Learn the Dibe Coding Methodology
- AI‑Native Coding Process: Dibe Coding
- Task Engineering in AI Coding: How to Break Problems Into AI‑Ready Pieces
- Structured AI Coding with Task Context: A Better Way to Work with AI Agents
- Dibe Coding: The Developer‑First Approach to AI‑native Development
- Delegating Code Tasks to AI: From Challenge to Breakthrough
