Can AI Really Write Production Ready Code?
The rise of artificial intelligence is rapidly reshaping the software development landscape. Developers increasingly rely on AI assistants for everyday coding tasks, making the process smoother. However, a significant question emerges from this trend. Can we move beyond simple assistance to build entire production ready applications using LLM generated code? This article delves into that very question. We will document an experiment to construct a complete project using only code written by Large Language Models.
This exploration is more than just a technical challenge. Consequently, it represents a critical look at the future of programming and the broader tech impact. We investigate whether models from Anthropic and Google can produce genuinely high quality, reliable code for real world use. The potential for accelerated development is immense, yet it is essential to proceed with caution. Therefore, this analysis will also uncover the hidden costs and practical difficulties of relying on AI for complex projects. Join us as we explore this new frontier, examining the results and considering what AI native development truly means for the industry.
The Grand Experiment: Building with 100% AI Code
To truly test the capabilities of modern AI, we embarked on an ambitious project. The goal was to build a reactive UI framework, named Lightview, using exclusively code generated by Large Language Models. This experiment, conducted over four weeks on a part time basis, aimed to push the boundaries of what AI can achieve in a production like setting. The final result was a substantial codebase. It included over 40 distinct UI components, spread across 60 JavaScript files, 78 HTML files, and 5 CSS files. In total, the project amounted to 41,405 lines of code, a significant undertaking for any developer, let alone an AI. For this task, we primarily utilized models from Anthropic, including Claude Opus and Claude Sonnet, along with Google’s Gemini Pro and Gemini Flash.
Assessing the Quality of LLM-generated code
The experiment provided deep insights into the quality and reliability of LLM-generated code. We found that the models could indeed produce high quality, functional code. However, this success was heavily dependent on the quality of the prompts and the guidance provided. Much like junior developers, the LLMs required clear, specific instructions to generate the desired output. They excelled at boilerplate tasks and creating self contained components. For instance, generating a standard button component with various states was a straightforward task. Yet, when dealing with more complex state management or unconventional UI patterns, the models sometimes struggled. They would produce code that was syntactically correct but logically flawed, leading to subtle bugs. This highlights a crucial point: while AI can write the code, a human expert is still needed to architect the system and validate the output. The process became a partnership, with the human guiding the AI’s vast but sometimes unfocused capabilities. The quality was there, but it needed careful cultivation.
The Hidden Costs and Challenges of AI in Coding
The promise of AI accelerated development is compelling, yet it is crucial to look beyond the surface benefits. Relying on AI for coding introduces a new set of challenges and hidden costs that are not immediately obvious. One of the most significant is the profound need for expert human oversight. As has been noted,
“LLMs can produce quality code—but like human developers, they need the right guidance.”
The AI acts as a powerful engine, but it requires a skilled driver to steer it. Without clear, precise, and well structured prompts, the models can generate code that is inefficient, insecure, or simply incorrect. This means the time saved in writing code can sometimes be lost in debugging and refining prompts.
Furthermore, there is the issue of error propagation at scale. A human developer might make a mistake in one part of a project. However, an AI can replicate a flawed pattern across the entire codebase in an instant. This leads to a dangerous scalability of mistakes. A small logical error can become a systemic problem that is incredibly difficult to trace and fix. This reflects the idea that,
“In many ways LLMs behave like the average of the humans who trained them—they make similar mistakes, but much faster and at greater scale.”
Beyond the code itself, we must also consider the significant resource consumption. Training and running these large language models require immense computational power, which translates to high energy and financial costs. These factors must be weighed against the productivity gains to understand the true cost of AI native development.
A Comparative Look at AI and Web Development Tools
To better understand the ecosystem in which this experiment took place, here is a breakdown of the key technologies and models involved. This includes both the AI models used for code generation and some of the modern web frameworks that represent alternative approaches to building user interfaces.
| Tool/Framework | Purpose | Origin Company | Key Features |
|---|---|---|---|
| Lightview | Reactive UI framework | LLM Generated | 100% AI generated, 40+ UI components, component based |
| Bau.js | Lightweight UI library | Open Source | Minimalist, reactive programming, easy to learn |
| HTMX | Frontend library | Open Source | Extends HTML, reduces JavaScript, simplifies dynamic pages |
| Juris.js | Specialized JavaScript library | Open Source | Task specific utilities, lightweight footprint |
| Claude Opus | Advanced LLM | Anthropic | High reasoning, large context window, powerful code generation |
| Claude Sonnet | Balanced LLM | Anthropic | Fast performance, cost effective, ideal for scaled workloads |
| Gemini 3 Pro | Multimodal LLM | Advanced reasoning, handles multiple data types | |
| Gemini Flash | Fast and efficient LLM | Optimized for speed, suitable for high frequency tasks |
The Future is a Partnership: Human Expertise and AI Code
The journey of building a complete UI framework with LLM generated code reveals a complex yet promising future for software development. Our experiment confirms that AI models are capable of producing substantial, functional codebases, potentially revolutionizing development speed. The creation of the Lightview framework is a testament to this raw capability.
However, this power must be wielded with wisdom. The success of AI in coding is not about replacing human developers but augmenting them. As we have seen, the quality of AI output is directly tied to the quality of human input. Therefore, cautious optimism is the most sensible path forward. The real tech impact will come from a collaborative partnership between human experts and AI tools.
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Frequently Asked Questions
What is LLM generated code?
LLM generated code is any software code written by a Large Language Model. Instead of a human developer writing each line, they provide natural language instructions or prompts to an AI like Anthropic’s Claude or Google’s Gemini. The AI then produces the code based on its vast training data. This can range from a small function to a complete application.
Can AI write production quality code?
Yes, our experiment demonstrated that AI can write functional, production ready code. However, the quality heavily depends on the guidance it receives. Think of the AI as a very fast junior developer. It needs clear, specific, and well structured prompts from an experienced human developer to produce code that is both reliable and efficient. The potential is there, but it requires expert oversight.
What are the main challenges when using AI for coding?
The biggest challenges are not in the code generation itself but in the process surrounding it. Firstly, there is the need for constant human supervision to guide the AI and validate its output. Secondly, AI models can propagate errors at a massive scale. A single flawed instruction can lead to a systemic issue across the entire codebase. Finally, these models require significant computational power, which has environmental and financial costs.
How do different AI models like Claude and Gemini compare for coding?
Different models are suited for different tasks. Advanced models like Claude Opus excel at complex reasoning and understanding large contexts, making them great for architectural planning. On the other hand, faster models like Gemini Flash are optimized for speed, making them better for repetitive tasks or generating boilerplate code quickly. The choice depends on the specific needs of the project.
Will AI replace human developers?
The most likely outcome is that AI will augment human developers, not replace them. The future of software development will probably be a partnership. Human developers will focus on high level system design, architecture, and creative problem solving, while AI will handle much of the day to day code writing. This collaboration will make development faster and more efficient.