On Using LLMs to Write Code

Peter Naur’s seminal 1985 essay, Programming as Theory Building, posits that programming transcends mere code creation; it’s fundamentally about constructing a “theory”—a deep, often tacit understanding of a system that resides in the minds of its developers. This perspective challenges the notion that code alone encapsulates a program’s essence, emphasizing that the true value lies in the shared mental models and insights of developer or the development team.

In the contemporary landscape, the advent of Large Language Models (LLMs) and the rise of “vibe coding”, a practice where developers exclusively use natural language prompts to generate code via AI, introduce new dynamics to software development. While these tools offer unprecedented efficiency, they also raise questions about the depth of understanding and theory building in the development process.

The Essence of Theory in Programming

Naur argues that a program is not merely its source code but a theory constructed by its developers. This theory encompasses the rationale behind design decisions, the understanding of problem domains, and the anticipated interactions within the system. Such knowledge is often implicit and cannot be fully captured through documentation or code alone. Consequently, when the original developers depart, the nuanced understanding—the theory—may be lost, making maintenance and evolution of the software challenging.

Vibe Coding: Efficiency Meets Abstraction

Vibe coding, popularized by figures like Andrej Karpathy, leverages LLMs to translate high-level prompts into executable code. This approach democratizes coding, enabling individuals with limited programming expertise to develop functional software. For instance, entrepreneurs like Therese Waechter have utilized AI tools to customize e-commerce platforms, enhancing business operations without deep technical involvement. 

However, this abstraction can lead to a superficial grasp of the underlying systems. Without engaging in the problem-solving and decision-making processes inherent in traditional programming, developers may miss out on building the comprehensive theories that Naur deems essential.

The Illusion of Understanding in AI-Generated Code

While LLMs can produce syntactically correct and functional code, they lack genuine understanding. As highlighted in critiques like “Go read Peter Naur’s ‘Programming as Theory Building’ and then come back and tell me that LLMs can replace human programmers”, LLMs operate by pattern recognition, not by constructing theories. They generate code based on learned correlations, without comprehending the problem domain or the implications of design choices. 

This distinction is crucial. Relying solely on AI-generated code without a foundational understanding can lead to systems that are insecure, difficult to maintain, adapt, or debug, especially when unforeseen issues arise.

Bridging the Gap: Integrating AI with Human Insight

To harness the benefits of vibe coding while preserving the depth of understanding emphasized by Naur, a balanced approach is necessary:

• Collaborative Development: Use LLMs as assistants to handle boilerplate or repetitive tasks, freeing developers to focus on complex problem-solving and theory building.
• Continuous Learning: Encourage developers to review and understand AI-generated code, fostering the development of internal theories about the system’s behavior and structure.
• Documentation and Knowledge Sharing: Maintain comprehensive documentation that captures not just the “how” but the “why” behind code, facilitating knowledge transfer and theory reconstruction.
• Iterative Refinement: Treat AI-generated code as a starting point, subject to human refinement and contextual adaptation.

Conclusion

While vibe coding and LLMs introduce powerful tools for rapid development, they do not replace the need for deep understanding and theory building in programming. Embracing these technologies should not come at the expense of the cognitive processes that enable developers to create, maintain, and evolve complex systems effectively. By integrating AI capabilities with human insight, we can achieve a synergy that leverages efficiency without sacrificing depth.

For a deeper exploration of these themes, consider reading Peter Naur’s original essay: Programming as Theory Building. It's worth the time!