Reddit Post: Developers Need Better AI Coding Practices, Not Just Better Tools

The Problem: Raw Prompting

The author identifies a common pattern where developers approach AI coding tools with what they call "raw prompting." This involves giving vague, open-ended instructions like "Add authentication to this app" without providing context, structure, or guardrails. When the AI makes assumptions, ignores existing code style, skips test coverage, and produces what the community calls "slop," developers tend to blame the tool rather than their own approach.

The Solution: Structured Approaches

The post outlines two levels of improvement for getting fit-for-purpose logic from Claude:

Level 1: Scaffolding (CLAUDE.md)

Stop making the AI guess your preferences. Define core principles in the project by documenting:

• Commit style
• Development approach
• Test methodology
• Code review standards

Set baseline expectations before asking the AI to write any code.

Level 2: Skills & Workflows

Instead of open-ended tasks, use opinionated structures like the Superpowers skillset on GitHub. Force the AI to:

• Analyze context and ask clarifying questions first
• Propose 3 distinct approaches and let you pick/adjust the architecture
• Step through the design one piece at a time
• Execute, run tests in isolation, and critique its own work against the original plan

Key Insight

The author compares the situation to working with a human junior developer: if you told them to "add auth" with zero context, you'd get a mess too. The AI isn't a mind reader. To get production-ready code, you need to provide management and accountability through proper prompting practices.