AI Generated Code = Codebase (Framework) Quality.

Matviy Cherniavski

Mar 31 (edited) • Stop the Slop

Example of the Task: “Add pagination to the users endpoint.”

Codebase 1 result:

• Follows existing patterns

• Reuses shared helpers

• Places the code in the correct module

• Respects naming conventions

• Integrates cleanly with current architecture

Codebase 2 result:

• Hardcoded values

• Duplicated logic

• File dropped in the wrong place

• Ignores existing abstractions

• Creates inconsistency that will cost more later

Same model and prompt, but the completely different output.

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🧠 The Real Variable Is the Codebase

A major factor affecting AI-generated code is what the AI agent can see and pattern-match against. AI coding agents amplify the quality (or the mess) that already exists.

A clean, consistent codebase gives the agent strong examples to follow. Meanwhile, a messy one forces it to invent things on the fly and invention is where most of the slop comes from.

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📁 What High-Quality Codebases Give the Agent

When the repository has clear design principles and consistent patterns, the agent can do the following well:

⤷ Reuse existing abstractions instead of reinventing them

⤷ Place new code in the right location

⤷ Follow established naming and structure

⤷ Respect architectural boundaries

⤷ Produce changes that feel like they belong

When those things are missing, the agent has almost nothing reliable to anchor on. So it starts guessing.

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⚙️ Design Principles That Matter More Now

In the AI agent era, certain engineering practices become even more valuable: