Speed without standards is technical debt
AI-generated code is useful only when it follows architecture constraints and review discipline.
BUNKROS AI Training
Ship cleaner software faster with AI-assisted engineering workflows.
Use AI for design, coding, reviews, tests, and debugging while preserving code quality, security, and maintainability.
Why This Matters
Strategic relevance before tactical execution.
Prompt quality shapes code quality
Engineering prompts require context, boundaries, and acceptance criteria.
Teams need shared patterns
Consistent workflows prevent code-style drift and hidden security issues.
What You Will Learn
Practical capabilities you can apply immediately.
- Create coding prompts with architecture and quality constraints.
- Use AI for refactoring without introducing regression risk.
- Generate tests and edge-case scenarios from requirements.
- Perform AI-assisted code reviews with security focus.
- Implement debugging workflows that isolate root causes faster.
- Build team-level standards for AI coding assistants.
Curriculum Modules
A structured path from foundations to implementation.
Module 1: Engineering Prompt Foundations
Define robust prompt structures for maintainable code output.
Module 2: AI Pair Programming Workflows
Use AI for implementation while preserving architecture consistency.
Module 3: Testing and Verification
Generate tests, assertions, and quality gates from specs.
Module 4: Secure Code Generation
Catch insecure patterns and enforce review controls.
Module 5: Refactoring and Legacy Modernization
Accelerate cleanup and migration tasks safely.
Module 6: Team Standards and Governance
Operationalize AI coding policies across repositories and teams.
30-Minute Training
One focused sprint to move from theory to repeatable execution.
00:00 - 05:00
Introduction
Define the problem this track solves, pick one real workflow, and set a measurable target for the session.
05:00 - 11:00
Theory Block 1
Map the core principles so your decisions are based on system behavior, not trial-and-error prompting.
11:00 - 17:00
Exercise Block 1
Run a controlled build task with explicit constraints, then measure output quality against your rubric.
17:00 - 23:00
Theory Block 2
Add governance, validation, and failure modes so the workflow remains usable in production.
23:00 - 30:00
Exercise Block 2 + Check
Refine your first build, run a quick knowledge check, and prepare your next learning sprint.
Theory Blocks
Foundations that keep your outputs reliable.
Speed without standards is technical debt
AI-generated code is useful only when it follows architecture constraints and review discipline.
Prompt quality shapes code quality
Engineering prompts require context, boundaries, and acceptance criteria.
Teams need shared patterns
Consistent workflows prevent code-style drift and hidden security issues.
Hands-On Exercises
Short builds designed for immediate skill transfer.
Exercise 1: Module 1: Engineering Prompt Foundations
Define robust prompt structures for maintainable code output.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 2: Module 2: AI Pair Programming Workflows
Use AI for implementation while preserving architecture consistency.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 3: Module 3: Testing and Verification
Generate tests, assertions, and quality gates from specs.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Knowledge Check
Validate comprehension before scaling the workflow.
Open Resources
Continue learning with high-quality public material.
Glossary
Key terms you should be fluent in for this track.
Workflow Constraint
A rule that limits ambiguity and keeps output behavior stable across runs.
Quality Gate
A mandatory review checkpoint before downstream use or publication.
Tools Covered
Tooling choices tied to workflow outcomes.
GitHub Copilot
ChatGPT
Claude
Cursor
VS Code
SonarQube
Jest
Playwright
Snyk
Who This Is For
Built for operators, builders, and strategic teams.
- Developers integrating AI into daily engineering workflows.
- Engineering managers defining AI coding standards.
- Product teams shipping AI-accelerated features.
- Technical founders balancing speed and software quality.
Outcomes and Career Impact
Execution outcomes with direct professional value.
Outcome
Increase development velocity without compromising code quality.
Outcome
Improve test coverage and reduce avoidable regressions.
Outcome
Establish security-aware AI coding standards.
Outcome
Create a reusable AI engineering playbook for your team.
Signals from Practice
Operator-level feedback and implementation sentiment.
"Our pull requests became faster and cleaner after this course."
"The debugging framework alone paid for the program."
Access Models
Free, cohort, and enterprise pathways.
Starter
EUR 0
AI coding checklist and prompt starter pack.
Pro Cohort
EUR 649
6-week sprint with review and feedback sessions.
Enterprise
Custom
Team rollout with repository standards and coaching.
Ready to Start
Engineer faster without sacrificing code integrity.
Join the code generation sprint and standardize AI-assisted development across your stack.
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