This article is part of the Prompt Driven Development series from the VS Code YouTube channel. It is based on the video Introduction to Prompt Driven Development and explains the concepts and workflows demonstrated, with a focus on how prompt driven development can be applied in practice using Visual Studio Code and GitHub Copilot.
🎥 Video Reference
As AI tools become increasingly capable, developers are not only writing code differently but also rethinking how software is designed and built.
Terms such as vibe coding, prompt engineering, and prompt driven development are often used interchangeably. However, they represent distinct levels of structure, intent, and repeatability. Understanding these differences is essential to choosing the right approach for exploration, learning, or production ready systems.
This article explains these approaches and then highlights the key technical learnings demonstrated in the video.
🧠 Coding Styles in the Age of AI
✨ Vibe Coding
Vibe coding refers to an intuitive and exploratory way of working with AI. Developers rely on natural language prompts and personal intuition, allowing the AI to generate code based on intent rather than explicit structure.
This style is fast, creative, and useful for experimentation, but it often produces results that are hard to reproduce or maintain. Because prompts are informal and rarely documented, consistency becomes a challenge.
🎯 Prompt Engineering
Prompt engineering introduces intention and structure. Developers design prompts carefully to guide the AI toward specific and predictable outcomes.
This approach improves reliability and reduces randomness. However, prompts are still often treated as one off inputs, used once and discarded, rather than as reusable artifacts.
Prompt Driven Development
Prompt driven development treats prompts as first class technical artifacts.
In this approach, prompts are:
- Written intentionally
- Refined and iterated over time
- Documented and versioned
- Used to guide workflows, not just outputs
Rather than interacting with AI in isolated moments, developers design processes where prompts support planning, implementation, and iteration in a repeatable way.
📌 Five Key Learnings from the Video
1️⃣ Prompts Are Part of the Codebase
One of the main ideas demonstrated is that prompts should not be ephemeral. When prompts are saved, documented, and versioned, they capture architectural intent and decision making, just like code or design documents.
Using Markdown files inside the repository makes this process lightweight and easy to share.
2️⃣ Re Prompting Is an Expected and Valuable Practice
The video clearly shows that the first prompt is rarely sufficient.
Prompt driven development assumes iteration. Prompts improve through feedback, refinement, and clarification. This process is similar to refactoring code and should be treated with the same discipline.
Re prompting is not a failure. It is part of the workflow.
3️⃣ Documentation Can Drive Implementation
Instead of treating documentation as an afterthought, the workflow demonstrated uses documentation as the starting point for implementation.
By referencing a structured Markdown document, the AI can scaffold an application more consistently, reducing improvisation and making the process easier to reproduce.
4️⃣ AI Assisted Development Still Requires Validation
Even with AI generating code, traditional engineering practices remain essential.
The video reinforces the habit of:
- Installing dependencies locally
- Running and testing the application
- Reviewing behavior before committing changes
Prompt driven development enhances productivity, but it does not replace testing or validation.
5️⃣ Context Improves AI Output
Providing richer context leads to better results. The video demonstrates this through:
- Custom Chat Modes, which define purpose and output expectations
- Copilot Vision, which uses screenshots to reason about UI changes
By narrowing context and constraints, developers can guide AI systems more effectively and reduce ambiguity.
🛠️ Applying These Ideas in Practice
The workflow shown in the video combines several VS Code features to support prompt driven development:
- GitHub Copilot for ideation and implementation
- Markdown files for prompt documentation
- Custom Chat Modes for repeatable workflows
- Copilot Vision for context aware UI changes
Together, these tools help transform AI interactions into a structured and intentional development process.
Conclusion
Prompt driven development offers a practical way to integrate AI into software engineering without sacrificing rigor.
By treating prompts as structured, reusable artifacts, developers gain:
- Clearer workflows
- Better documentation
- More consistent outcomes
- Easier collaboration
For teams and individuals looking to move beyond ad hoc AI usage, prompt driven development provides a scalable and repeatable model grounded in existing engineering best practices.
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