How to stop AI from writing broken production code?

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PAGE: https://alison.com/course/vibe-coding-basics

PROMISE: Learn prompt frameworks to fix why your AI agent fails on complex debugging tasks

WHO: Intermediate developers using AI coding assistants who struggle with consistency

WHYNOW: AI coding tools are now standard, but most users lack the specific prompt skills to make them reliable

DESC: Enhance your software development skills by mastering AI-powered vibe coding, turning simple natural-language instructions into fully functional code.

BEFORE: You write ad hoc prompts that the agent interprets incorrectly

BEFORE: You assume the agent understands your codebase without explicit context

BEFORE: You trust the agent output without checking for standard AI errors

AFTER: You use structured frameworks to guide the agent toward correct solutions

AFTER: You explicitly define goals and styles to ensure the agent focuses correctly

AFTER: You review output against a list of common errors before integrating code

GAIN: A repeatable method for writing prompts that yield functional debugging code

GAIN: Clear criteria for evaluating whether an agent response is production-ready

GAIN: Awareness of specific failure modes in AI-generated software to mitigate

STEP: Your agent produces broken code when given vague debugging instructions || Explain how to craft effective prompts using structured frameworks || You structure debugging requests with clear goals and constraints for the agent

STEP: The agent misses the root cause because the context is incomplete || Identify the importance of clarity in defining goals, and styles within prompts || You define specific success criteria before running the agent to reduce drift

STEP: The agent introduces new bugs while trying to fix the old ones || Recognise common output errors in AI-generated software || You anticipate typical hallucinations and review the output for known pitfalls

NOTFOR: Developers who need a certified engineering degree rather than practical tips

NOTFOR: Teams that require deep theoretical computer science training over application

FACT: Average duration: 3-4 Hours (Contains Video)

FACT: CPD-accredited certificate available

FACT: Intermediate level

COVERS: Explain the concept of vibe coding and its philosophical shift in software development

COVERS: Distinguish between traditional coding practices and vibe coding methodologies

COVERS: Identify ethical considerations when using generative AI tools in vibe coding

COVERS: Explain the benefits of rapid prototyping through vibe coding

COVERS: Recognise the importance of human oversight and quality assurance in the vibe coding process

COVERS: Assess the risks and limitations of vibe coding

COVERS: Explain how to craft effective prompts using structured frameworks

COVERS: Identify the importance of clarity in defining goals, and styles within prompts

COVERS: Discuss how to use natural language prompts to request specific features

COVERS: Recognise common output errors in AI-generated software

COVERS: Apply descriptive terms to influence visual styling during prompt generation

COVERS: Explain how to prompt AI tools to build small games or utility apps

MODULE: Understanding Vibe Coding as a Creative Process || What Is Vibe Coding? | The Role of Generative AI Tools | Benefits and Limits of Vibe Coding

MODULE: Mastering Prompt-Based Development || Writing Effective Prompts | Debugging by Prompting | Refining UI and Design via Prompts | Applying Vibe Coding to Projects

What you are probably seeing

Your AI-generated features work in demo mode but crash in live environments because the model guessed at requirements.

You spend more time debugging and refactoring AI output than writing the code yourself, negating any time savings.

Why it happens

Large language models lack business context and will default to generic patterns that do not fit your specific system architecture.

Without explicit constraints, the model optimizes for plausible-sounding code rather than correct, secure, and maintainable solutions.

What changed recently

Step by step

1. Write a detailed specification document that defines user goals, edge cases, and failure states before opening the chat interface.

2. Provide the LLM with relevant existing codebase files to establish context and coding conventions.

3. Instruct the model to list assumptions and questions it has about the spec before generating any code.

4. Review the generated code against your specification line by line to verify every requirement is met.

5. Run automated tests on the new code and check for security vulnerabilities before merging it into your main branch.

Ways to go further

Better fit: Vibe Coding Basics For when you are debugging why your AI agent fails on complex tasks. Open the free course on Alison ↗

Best when: Prompt Engineering for AI Applications For when you need structured templates to define requirements before coding. Open the free course on Alison ↗

Affiliate links: CHEAPUNO may earn a commission if you buy through them, at no extra cost to you.

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