# The case for using AI to write better code more slowly

> Source: <https://dev.to/thegatewayguy/the-case-for-using-ai-to-write-better-code-more-slowly-10k3>
> Published: 2026-05-26 13:50:16+00:00

The dominant mental model for AI-assisted coding is speed: generate multi-hundred-line PRs, merge fast, iterate faster. Vibe coding as a velocity play.

Nolan Lawson's post this week pushes back on that — not by rejecting LLMs, but by using them differently.

"You can use them just as effectively to write high-quality code more slowly."

The hook is simple: LLMs are excellent at finding bugs. Anthropic's Mythos research showed agents can surface flaws in a codebase at scale. Lawson extended that insight into a practical PR review workflow — and the results are the opposite of slop.

Lawson runs a multi-agent review skill that throws Claude, Codex, and Cursor Bugbot at every PR independently, then consolidates findings ranked by severity: critical, high, medium, low.

The key design choice is the ensemble. Multiple models reviewing the same code self-correct each other — the false positive rate drops to near zero, while bug coverage stays high. A single model hallucinates; three models debating converge on real issues.

His triage loop once the report lands:

That last point is important. This workflow will sometimes tell you to throw away your work. That's a feature.

Velocity hasn't gone up. If anything, it's slower. The review process regularly surfaces pre-existing bugs, sending Lawson on side-quests to write unit tests and fix subtle flaws that predate the PR.

That's the point. Pre-LLM, understanding a codebase deeply meant exploring its failure modes — where the assumptions break down, where the edge cases bite. That's still the most valuable form of code knowledge. This workflow automates the *discovery* without removing the *depth*.

Lawson also suggests pairing this with understanding tools: have the agent explain how the PR works and where it might fail, generate Mermaid diagrams, or use Matt Pocock's `/grill-me`

skill until you can explain the entire changeset from memory.

The tools didn't change. The mental model did.

*✏️ Drafted with KewBot (AI), edited and approved by Drew.*