AI Doesn’t Replace Us, It Amplifies Us
AI Doesn’t Replace Us, It Amplifies Us
AI Doesn’t Replace Us, It Amplifies Us
When I first saw AI coding, it looked like “vibe coding” — projects that ran but lacked security, structure, and scalability. I assumed that was all AI could do. But with the right approach, it turned into an accelerator for building production-grade software.
When I first saw AI coding, it looked like “vibe coding” — projects that ran but lacked security, structure, and scalability. I assumed that was all AI could do. But with the right approach, it turned into an accelerator for building production-grade software.
When I first saw AI coding, it looked like “vibe coding” — projects that ran but lacked security, structure, and scalability. I assumed that was all AI could do. But with the right approach, it turned into an accelerator for building production-grade software.
Aug 30, 2025
4 minutes
The Turning Point: Prompt Engineering
At first, I’d throw vague requests at the AI and hope for the best. The results were messy and not production-ready.
The breakthrough came when I started applying prompt engineering. Setting strict rules around architecture, naming conventions, and security.
Suddenly, I could generate 75 to 80 percent of a secure, scalable project in hours. The remaining 20 to 25 percent was me, reviewing, refactoring, and fixing weak spots like:
Removing hardcoded API keys (replaced with environment variables).
Adding authentication and rate limiting to exposed endpoints.
Refactoring repetitive code into reusable modules.
Correcting oversimplified database schemas.
That’s when I realized, the issue wasn’t the AI. It was how I was using it.
Different Tools, Different Tradeoffs
Claude CLI → My favorite for rapid prototyping with precision. It doesn’t over-assume or skip steps. Some might find the down side of non-enterprise users facing 5 hour cooldowns. But I treat that as a feature. It forces me to step away, review the AI’s work, and return with fresh eyes. It creates a natural cycle: generate, pause, review, refine.
Gemini → The best free option. It’s not as controllable as Claude, but a solid way to experiment or prototype without paying. The tradeoff, long sessions can get buggy terminals and require resets.
ChatGPT (Codex/GitHub integration) → Great for snippets, refactors, and scaffolding tests, but less reliable as a full copilot due to assumptions and hallucinations.
Bottom line: Claude is my go-to for high quality prototyping, Gemini is the best free alternative, and ChatGPT shines as a snippet helper.
A Framework for Coding with AI
What made the biggest difference in my workflow was treating AI sessions like a development sprint.
I start projects with a guided setup prompt that:
Defines scope (frameworks, databases, integrations).
Enforces conventions (camelCase, environment variables, no secrets in repo).
Runs prerequisite checks (Node, Docker, Git, etc.).
Forces the AI to move step by step, no skipping, no assumptions.
This structure keeps me in control and ensures nothing important gets overlooked.
Where We’re Headed
A programmer isn’t just someone who writes code.
A programmer is someone who can take a problem, design a solution, and make it scalable, secure, and adaptable.
That skill doesn’t disappear with AI, it becomes more valuable.
The engineers who thrive will be those who can:
Guide AI with precision.
Spot flaws in its output.
Refine its work into production grade systems.
Conclusion
The difference between vibe coding and coding with AI is discipline.
Without structure, AI outputs shortcuts that don’t scale. With structure, AI becomes an accelerator, letting me move faster without sacrificing quality.
Without self-awareness and self-control, you can fall into the category of vibe coding. At the end of the day even if you provide the best prompt engineering, AIs are not perfect. Meaning it's important to supervise. Again; pause, review, refine.
For me, tools like Claude, Gemini, and ChatGPT aren’t replacements. They’re amplifiers. They don’t remove the need for strong engineering, they highlight it.
The future of programming won’t be about humans versus AI.
It will be about the developers who know how to make both work together.
The Turning Point: Prompt Engineering
At first, I’d throw vague requests at the AI and hope for the best. The results were messy and not production-ready.
The breakthrough came when I started applying prompt engineering. Setting strict rules around architecture, naming conventions, and security.
Suddenly, I could generate 75 to 80 percent of a secure, scalable project in hours. The remaining 20 to 25 percent was me, reviewing, refactoring, and fixing weak spots like:
Removing hardcoded API keys (replaced with environment variables).
Adding authentication and rate limiting to exposed endpoints.
Refactoring repetitive code into reusable modules.
Correcting oversimplified database schemas.
That’s when I realized, the issue wasn’t the AI. It was how I was using it.
Different Tools, Different Tradeoffs
Claude CLI → My favorite for rapid prototyping with precision. It doesn’t over-assume or skip steps. Some might find the down side of non-enterprise users facing 5 hour cooldowns. But I treat that as a feature. It forces me to step away, review the AI’s work, and return with fresh eyes. It creates a natural cycle: generate, pause, review, refine.
Gemini → The best free option. It’s not as controllable as Claude, but a solid way to experiment or prototype without paying. The tradeoff, long sessions can get buggy terminals and require resets.
ChatGPT (Codex/GitHub integration) → Great for snippets, refactors, and scaffolding tests, but less reliable as a full copilot due to assumptions and hallucinations.
Bottom line: Claude is my go-to for high quality prototyping, Gemini is the best free alternative, and ChatGPT shines as a snippet helper.
A Framework for Coding with AI
What made the biggest difference in my workflow was treating AI sessions like a development sprint.
I start projects with a guided setup prompt that:
Defines scope (frameworks, databases, integrations).
Enforces conventions (camelCase, environment variables, no secrets in repo).
Runs prerequisite checks (Node, Docker, Git, etc.).
Forces the AI to move step by step, no skipping, no assumptions.
This structure keeps me in control and ensures nothing important gets overlooked.
Where We’re Headed
A programmer isn’t just someone who writes code.
A programmer is someone who can take a problem, design a solution, and make it scalable, secure, and adaptable.
That skill doesn’t disappear with AI, it becomes more valuable.
The engineers who thrive will be those who can:
Guide AI with precision.
Spot flaws in its output.
Refine its work into production grade systems.
Conclusion
The difference between vibe coding and coding with AI is discipline.
Without structure, AI outputs shortcuts that don’t scale. With structure, AI becomes an accelerator, letting me move faster without sacrificing quality.
Without self-awareness and self-control, you can fall into the category of vibe coding. At the end of the day even if you provide the best prompt engineering, AIs are not perfect. Meaning it's important to supervise. Again; pause, review, refine.
For me, tools like Claude, Gemini, and ChatGPT aren’t replacements. They’re amplifiers. They don’t remove the need for strong engineering, they highlight it.
The future of programming won’t be about humans versus AI.
It will be about the developers who know how to make both work together.