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tech 26 May 2026

Using AI to Write Better Code More Slowly

AI isn't just for fast coding; it can also enhance code quality. Learn how to leverage language models for deep analysis and bug prioritization.

Article inspired by the original source
Using AI to write better code more slowly ↗ nolanlawson.com

Introduction

In a world where speed often takes precedence over quality, the idea of using Artificial Intelligence (AI) to write better code more slowly may seem counterintuitive. Yet, this approach might just revolutionize modern software development. Language models (LLMs) aren't just fast coding machines; they are powerful analytical tools that can enhance code quality when used wisely.

Why Slowing Down Can Be Beneficial

The prevalent notion that AI should only accelerate the coding process is widespread. However, savvy developers recognize the value of thorough examination and continuous improvement. By using AI to analyze existing code, subtle bugs that might otherwise go unnoticed can be uncovered. For instance, language models from Anthropic and OpenAI have shown effectiveness in detecting complex bugs.

Methodology: Multi-Layered Analysis

To maximize the potential of LLMs, it is crucial to integrate them into a rigorous code review process. By using multiple models, such as Claude, Codex, and Cursor Bugbot, developers can minimize false positives and properly prioritize bugs. This multi-layered approach is essential to avoid model hallucinations and ensure effective error validation.

Concrete Example

Consider a development team working on a SaaS application. By integrating LLMs into their workflow, they identified critical security vulnerabilities that had not been detected during manual reviews. Consequently, they not only improved their product's security but also bolstered customer trust.

The Optimal Workflow

The process starts with identifying critical and high-priority bugs using AI. These bugs are then fixed under the supervision of an experienced developer. Medium and low-priority bugs are addressed based on their potential impact. If a Pull Request (PR) contains too many critical issues, it is often better to completely rethink the approach.

Conclusion: The Importance of Quality

At a time when businesses are under pressure to deliver quickly, it is tempting to sacrifice quality for speed. However, poor-quality code can lead to costly problems in the long run. By using AI to slow down and improve the development process, companies can not only ensure quality but also maintain their competitive edge.

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