AI Programming Beyond Vibe Coding

December 17, 2025
8 min. read

It's interesting to see how AI tools have transformed programming workflows. A trend known as "vibe coding" has emerged, where developers rely heavily on AI to generate code snippets with minimal oversight. While this approach can speed up development, it can also lead to buggy or inefficient code if not carefully managed.

This post will explore the concept of vibe coding, its advantages and disadvantages, and strategies for more effective AI-assisted programming.

What is Vibe Coding? #

Vibe coding is a programming style where developers use AI tools to iteratively generate code based on prompts, often without deep understanding or thorough review of the generated code. The term "vibe" suggests a casual, almost carefree approach to coding, relying on the AI's capabilities to fill in gaps.

Why Is Vibe Coding Good? #

Vibe coding provides several advantages:

Speed: Vibe coding can significantly speed up the coding process, allowing developers to quickly generate boilerplate code or solve common problems.
Creativity: It can inspire new ideas or approaches that a developer might not have considered.
Lower entry barrier: It lowers the barrier to entry for less experienced programmers who can leverage AI to assist with coding tasks.
Focus on Higher-Level Problems: By offloading routine coding tasks to AI, developers can focus more on architecture and design.
Rapid Prototyping: Vibe coding allows for quick iteration and prototyping, enabling developers to test ideas rapidly without getting bogged down in implementation details.
Learning Aid: For beginners, vibe coding can serve as a learning tool, providing examples of code snippets and patterns that they can study and learn from.
Productivity Boost: It can help experienced developers overcome writer's block or fatigue by providing fresh code suggestions.

Where AI Generally Struggles #

Deeply complex systems

If you’re dealing with complex algorithms or novel problems that the AI likely hasn’t seen, it may struggle. For example, writing a brand-new algorithm from a research paper or doing something like writing a compiler or highly concurrent system — these involve intricate logic that requires true understanding and often creative leaps. AI can try, but it might get things subtly wrong.

Low-level optimizations and systems programming

Current AI models are primarily trained on high-level languages and abstractions. If you need to do low-level bit-twiddling, write highly optimized C code for a specific microcontroller , or generate vectorized SIMD instructions, the AI might not be reliable. It might produce code that looks plausible but isn’t truly optimal, or even correct, on a hardware level.

Unique or niche frameworks

If you’re using a very new or obscure framework that wasn’t around during the AI’s training at all, or if you're using a newer version of the framework than what was available during the AI's training, it won’t know about it. In such cases, the AI might produce code that looks like it fits but actually call functions that don’t exist (hallucinations) or use outdated versions of the APIs.

Interpreting intent and requirements

Sometimes AI struggles when requirements are implicit or contradictory. It has no true understanding of the end goal beyond what you explicitly tell it. If requirements are vague (“make it efficient” — what does that precisely mean?), the AI might guess incorrectly what you care about (memory versus speed, for instance).

AI can also misinterpret instructions if there’s domain-specific context (like business rules) it’s not aware of. It might produce a logically correct solution that doesn’t actually solve the problem because the nuance was lost in translation.

Hallucinations

A hallucination happens whenever the AI has to bridge a gap between the patterns it knows and the specific answer you need. The AI is a statistical "next-word" predictor, not a fact-checker. If it predicts that a plausible-sounding-but-fake answer is the most likely type of text to come next, it will generate incorrect results. This "gap" can be caused by any of its weaknesses:

Creative UI/UX: You ask for a "novel UI element." It's never seen one, so it confidently invents a plausible-sounding (but non-existent) CSS property or component name.

Low-Level Code: You ask for an optimization for a brand-new processor. It doesn't know the new instruction set, so it hallucinates a function call that looks like a real one but will fail to compile.

Complex Systems: You ask for a niche algorithm. It's only seen bits and pieces, so it confidently mixes them into a new, subtly broken algorithm.

Interpreting Intent: Your prompt is slightly vague. The AI invents a business requirement you never stated and codes for that instead.

Stale Training Data

AI models are trained on data up to a certain cutoff date. If the programming language, framework, or library has changed significantly since then, the AI may suggest outdated or deprecated practices.

Unique Frameworks: This is the most obvious one. It recommends version 1.0 of a library when version 4.0 is current, using deprecated methods.

Low-Level Code: It doesn't know about the new security vulnerabilities (like Spectre/Meltdown) discovered last year, so it generates code that is now considered unsafe.

Complex Systems: It suggests an architectural pattern (e.g., for microservices) that was popular in 2023 but has since been replaced by a more efficient best practice.

Creative UI/UX: It keeps generating UI designs that look like they're from 2022, unaware of new design trends, color palettes, or accessibility standards that emerged since.

Rehash Loops (Getting stuck)

A rehash loop is a signal that the AI has "run out of ideas" based on your prompt and its training. It's stuck in a local maximum. The AI lacks true understanding. It can't step back, re-evaluate the problem from a new angle, and "think" its way out. It can only follow the statistical path of your prompts, and if those prompts keep leading it down the same path, it will keep walking in circles.

Creative UI/UX: You ask for "something different" for a login page. It gives you a login with a username/password. You say "no, more creative." It gives you a login with a "forgot password" link. You say "no, really different." It gives you a login with a "Sign in with Google" button. It's stuck in the "login page" pattern and can't break out to a new concept (like a magic link or passwordless auth).

Complex Systems: You're debugging a complex algorithm. The AI gives you a fix that doesn't work. You tell it "that's wrong, try again." It gives you the exact same fix but with different variable names. It has exhausted its shallow understanding of your complex problem.

Interpreting Intent: The AI misunderstands your goal. You try to re-phrase your prompt, but you keep using the same core keywords. The AI latches onto those keywords and keeps giving you variations of the same wrong answer.

Problems With Vibe Coding #

The advantages we discussed earlier can also lead to pitfalls and bad habits:

Overconfidence: Shows up when developers treat AI suggestions as correct without the usual validation, testing, or design review. These risks can trip up experienced developers, especially under delivery pressure. They routinely show up in real AI-assisted coding sessions and can be surprisingly consistent.
Complacency: Relying too much on AI can lead to complacency, where developers stop critically evaluating the code and accept AI-generated solutions without question.
Lack of Understanding: Vibe coding can result in a lack of deep understanding of the codebase, as developers may not fully grasp the logic behind AI-generated code or may not be familiar with the language or frameworks used in the project.
Overreliance on AI: Developers might become overly dependent on AI tools, potentially diminishing their problem-solving skills and creativity.
Quality Issues: The code generated through vibe coding may not adhere to best practices, leading to maintainability and scalability issues in the long run, particularly if the AI is not properly guided.
Integration Challenges: AI-generated code may not seamlessly integrate with existing codebases, leading to compatibility issues or technical debt.

Strategies for AI-Assisted Programming Beyond Vibe Coding #

These are some of the strategies to get more reliable results when using AI for programming:

Research The Subject #

Research makes sure the AI isn’t your only source of truth or, at least, be proficient enough to know when to doubt the answers you get.

When you rely solely on AI, you risk compounding errors—the AI makes an assumption, you build on it, and soon you’re deep in a solution that doesn’t match reality.

Cross-checking with documentation or even asking a different AI can reveal when you’re being led astray.

Provide Context #

Always provide the context the AI needs to give relevant answers so you can avoid poor outputs. This includes details about the project, coding standards, performance requirements, and any constraints. The more context you provide, the better the AI can tailor its responses to your specific needs.

Sometimes it's tempting to just ask your AI to "write a function that does X." The AI will happily comply but the answer, while it is correct and works in isolation, may not fit well, or at all, into the actual project you're building.

If you provide context about how that function fits into the larger system, what constraints it has (performance, security, etc.), and any relevant standards or patterns your team follows, the AI can generate code that aligns better with your needs.

Framing and Refining #

Framing is about asking questions that set up useful answers.

If you ask generic questions you get generic answers. For example, “How do I handle errors?” gets you a try-catch block. “How do I handle network timeout errors in a distributed system where partial failures need rollback?” gets you circuit breakers and compensation patterns.

Refining means not settling for the first thing the AI gives you.

The first response is rarely the best—it’s just the AI’s initial attempt. When you iterate, you’re steering toward better patterns and results. Refining moves you from “This works” to “This is actually good.”

Sometimes the AI needs a different perspective.

If you're working on a web app and the AI keeps presenting the same or very similar results, it may be time to switch context. Explicitly state in your prompt that you're looking for a different approach or perspective.

Critical Thinking #

Critical thinking ties it all together.

You should never stop asking whether the code actually works for your project. It’s debugging the AI’s assumptions, reviewing for maintainability.

You should also ask, “Will this make sense six months from now?”

Code Reviews, Testing and Validation #

AI code should go through the same code review and testing process as the code you add to your application including unit tests, integration tests, and performance testing.

Treat AI-generated code as you would any other code to ensure it meets your quality standards

Conclusion: Vibe Coding and Beyond #

Vibe coding can be useful for rapid prototyping and generating boilerplate code, but it comes with significant risks if not managed carefully. By understanding the limitations of AI, providing context, framing questions effectively, and applying critical thinking, developers can move beyond vibe coding to create robust, maintainable software.

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