About the course
The ease of AI code generation has created a new category of technical debt: Vibe Code. This is code that is syntactically correct and appears functional during a "vibe check" but often lacks architectural integrity, ignores edge cases, or introduces "ghost dependencies." As the volume of code generated by LLMs increases - a phenomenon known as the Jevons Paradox of AI - the role of the senior developer is shifting from writer to editor-in-chief.
This engaging workshop is designed for mid-to-senior developers and team leads who find themselves "prompting more than typing" but are feeling the weight of increasingly brittle codebases. We provide a rigorous framework for the "Hardening Process" - the transition from AI-generated raw output to hardened, production-ready systems.
Platform-Agnostic with Deep Integration: Our delivery is fully customisable based on your team's specific tech stack and preferred AI tooling. We incorporate best practices for leading AI-augmented platforms, including:
Cursor & VS Code Copilot (Composer and Chat-driven workflows)
GitHub Copilot & Copilot Extensions
Replit Agent & Ghostwriter
Supermaven, Cody (Sourcegraph), and Tabnine
Claude Sonnet / GPT-4o / DeepSeek (Model-specific prompting for architecture)
Workshop Duration Options
The Intensive (1 Day): 7 hours of rapid-fire theory, anti-pattern recognition, and structured hardening labs. Best for experienced teams looking for immediate process upgrades.
The Masterclass (2 Days): Expands the duration two 2 days, with a midway section dedicated to a "Bring Your Own Code" (BYOC) lab. Participants refactor actual "vibe code" from their own repositories under guided supervision.
The Executive Briefing (Half-Day): A high-level 3.5-hour version focused on Module 1 and Module 3, designed specifically for Team Leads and Engineering Managers.
Instructor-led online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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By the end of this course, attendees will be able to:
- Identify AI "Drift": Spot where an LLM has lost the architectural thread due to context window limitations.
- Perform "Vibe" Audits: Conduct code reviews that specifically target AI-specific smells and hallucinated logic.
- Enforce System Boundaries: Use advanced type-safety and schemas to "cage" AI output within valid parameters.
- Automate Quality Control: Build CI/CD guardrails that catch "lazy" AI habits before they reach production.
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Mid-to-Senior Developers: Who use AI tools daily and want to maintain high code quality.
Team Leads & Architects: Tasked with managing the technical debt generated by AI-assisted teams.
SREs & QA Engineers: Needing to understand the failure modes of non-human-authored code.
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Those looking to participate in the hands-on elements of this workshop should ideally have:
Professional Development Experience: Strong comfort in at least one major stack (e.g., TypeScript, Python, C#, or Java).
Architectural Knowledge: A solid understanding of Design Patterns and SOLID principles.
We can customise the training to match your team's experience and needs - with more time and coverage of fundamentals for newer developers, or a swifter pace for experienced coders.
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This vibe code refactoring course is available for private / custom delivery for your team - face-to-face, on-site at your location of choice, or remotely via MS Teams or your own platform of choice - get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Module 1: Anatomy of a "Vibe"
Identifying "Hallucinated Logic": Recognizing code that looks syntactically perfect but follows a logic that doesn't exist in your specific stack or library version.
The "Context Window" Trap: Why AI loses the thread on large-scale architectures and how to spot the "drift" in long files or complex multi-file changes.
Code Smell 2.0: Identifying AI-specific anti-patterns (Over-engineered loops, Redundant utility functions, "Lazy" error handling).
Module 2: The Hardening Process
Structural Integrity: Techniques for stripping "Vibe Code" down to its functional core and re-fitting it into your existing design patterns (SOLID, DRY, etc.).
Dependency Audit: Cleaning up "ghost dependencies" and random libraries suggested by LLMs for simple problems.
Type Safety & Contracts: Using TypeScript or Pydantic to "force" AI output to behave within the boundaries of your system.
Special Session: Optional Day – BYOC (Bring Your Own Code)
Hands-on Workshop: Students bring a specific AI-generated script or component they are currently struggling to maintain.
Peer Review: Group analysis of the "vibe" level and identifying the highest-leverage refactoring targets.
Live Refactoring: Intensive 1-on-1 and group coaching to convert the "vibe code" into a production-ready module.
Module 3: Automated Guardrails
Unit Testing the "Black Box": Strategies for writing comprehensive tests for code you didn't technically write yourself.
Snapshot Testing for Logic: Using AI to generate initial test suites—and the critical methodology for verifying both the test and the code.
Linting for Intent: Setting up custom rules (ESLint, Ruff, etc.) to catch common LLM "lazy" habits.
Module 4: The Post-AI Workflow
The "Editor-in-Chief" Mindset: Shifting team culture from "generating more" to "maintaining better."
Reviewer Training: How to conduct a PR review for AI-assisted code.
Documentation as Truth: Ensuring the AI maintains the "Why" through synchronized TSDoc/JSDoc.
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An AI-native code editor branched from VS Code that deeply integrates LLM capabilities for seamless refactoring and codebase navigation.
https://code.visualstudio.com/docs/copilot/overview
The official extension that brings GitHub Copilot’s autocomplete, chat, and refactoring tools directly into the Visual Studio Code interface.
https://github.com/features/copilot
An industry-leading AI pair programmer that provides real-time code suggestions and architectural guidance across multiple IDEs.
A high-speed AI code completion tool featuring a massive one-million-token context window for understanding large-scale projects.
An AI coding assistant that specializes in reading and understanding your entire codebase to answer complex questions and write code in context.
An AI assistant for software developers that emphasizes privacy and security with local model hosting and private code training options.
https://www.anthropic.com/claude/sonnet
Anthropic’s high-performance model known for its advanced reasoning, coding capabilities, and nuanced adherence to complex instructions.
https://developers.openai.com/
The developer platform for integrating GPT-4o and other cutting-edge models into custom applications and automated workflows.
An open-source AI platform providing powerful, cost-effective models specialized in high-level coding tasks and mathematical reasoning.
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