透過AI規則，從零散程式碼邁向一致性輸出

From fragmented code to consistent output with AI rules

Most developers use AI without structured guidance. Here's how capability-specific rules change everything.

Roni Ström

Founder

Most developers use AI without structured guidance. They type prompts, hope the output is usable, and spend time fixing what comes back. This works, but you're leaving a lot on the table.

I've spent the past year doing AI-assisted development. The single biggest productivity lever: investing in rules.

Every tool calls them something different. Cursor Rules. Claude Skills. Windsurf Rules. Copilot Instructions. Junie Guidelines. Same concept: persistent instructions that guide the AI across sessions.

The four maturity levels

How developers provide context to AI varies widely. Here's what I've observed:

Level 1: No rules

This is most common. Developers interact through conversation, prompting back and forth until they get what they need. When they move to another task, everything resets. The AI doesn't know your architecture, your patterns, or your constraints. Each session starts from zero.

The consequence: fragmented codebases. Architecture decisions left to the AI. Inconsistent patterns across files. You're treating the AI as a creative partner when you want a code factory with low tolerances.

Level 2: Architecture docs as rules

Some developers add existing architecture documentation as context. The problem: architecture docs are written for humans to read. They explain concepts and rationale, not step-by-step instructions.

Human docs explain what and why. Machine instructions need how, with code samples.

Level 3: AGENTS.md files

More teams now create AGENTS.md files describing the tech stack, folder structure, key patterns, and common commands. These help, but they're too high-level:

This tells the AI about the project without giving it specific guidance to produce consistent, correct code. The AI still has to guess at patterns.

Level 4: Capability-specific rules

This is where productivity jumps. Each rule file focuses on one architecture capability:

This example shows runtime architecture capabilities for a Next.js + Supabase web app. If you're building native mobile apps, backend services, or infrastructure code, your capabilities will look different.

I recommend creating separate rulesets for different architecture layers:

Each layer has its own patterns.

The principle stays the same: focused rules for specific capabilities. Related capabilities can share a rule when they naturally go together.

Each rule file:

Anatomy of a good rule

Rules have two parts: front matter and content.

Front matter controls when the rule loads. The globs field tells the AI when this rule is relevant. Form rules load when you're working on TSX files. Database rules load when touching the data layer. This saves context window for what matters right now.

Content gives the AI explicit instructions and code examples. Not prose explaining concepts. Actual patterns to follow.

Generic vs capability-specific rules

You need both. Generic rules for broad patterns, capability rules for specific workflows.

Generic rules cover broad concerns: language conventions, framework patterns, project structure, code style. Some always apply, others load for matching file types.

Here's a project structure rule that always applies:

No code examples needed. The AI just needs to know where to put things.

Here's a TypeScript rule that loads for matching files:

Short. Direct. Front matter plus instructions with one code example.

Capability rules go deeper on specific architecture concerns. Here's an excerpt from a forms rule:

Here's another capability rule for API routes:

Capability rules cover complete workflows. The AI gets the exact pattern, not just principles.

The feedback loop

When the AI makes a mistake, treat it as a trigger. Go back to your rules and improve them so it doesn't happen again.

AI suggesting inline styles instead of your design system? Add a rule. Wrong import paths? Add a rule. Inconsistent error handling? Add a rule.

Over time, your rules get better. The AI makes fewer mistakes. You spend less time fixing output.

Getting started

If you're new to rules, start with generic rules covering your language and framework conventions. Add capability rules as you notice the AI making repeated mistakes in specific areas.

Two resources:

Rules don't replace architecture

You still need architecture documentation for humans. Teams need shared understanding of design decisions, tradeoffs, constraints.

Rules translate those human decisions into machine-readable instructions. Architecture docs explain why you chose a pattern. Rules tell the AI how to implement it.

If you take one thing from this post: invest in your rules. The upfront effort pays back every time you prompt.

SaaS products and consulting services.

Quick Links

Products

© 2026 Ström Capital Oy. All rights reserved.

Business ID: 3466634-5 · E-invoice: 003734666345 · Operator: 003723327487 (APIX)