Structured rules, faster AI coding.

Make development faster and more efficient with tailored instructions.

Overview

RulesForAI is a developer-focused web product that helps users create Rules and Skills for AI coding assistants.
The core value is consistency: instead of relying on ad-hoc prompts, users generate standardized instructions that are easier to reuse across projects.

I owned the full lifecycle: product direction, UX, architecture, full-stack implementation, and ongoing iteration.

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Problem

Developers adopting AI coding tools often face the same bottleneck: outputs are inconsistent because team standards and project constraints are not encoded clearly.

Common pain points:

• Prompt quality varied by person and by day.
• Important constraints (stack, architecture, style, security) were frequently omitted.
• Teams repeatedly rewrote similar instructions for every new project.
• AI outputs looked “technically correct” but misaligned with project conventions.

In short: teams had AI access, but not AI operational discipline.

Goals

• Make it easy to convert natural-language context into structured rule sets.
• Improve consistency and quality of AI-generated code outputs.
• Reduce setup time when starting new projects.
• Keep the UX simple enough for solo developers and small teams.

Solution

I designed RulesForAI around a guided generation flow:

1. User provides project context (stack, architecture preferences, coding standards, constraints).
2. The platform structures that context into a normalized format.
3. The system generates reusable rules/skills optimized for coding agents.
4. Users refine and export outputs for practical use in their workflow.

Key product choices:

• Prioritized clarity over feature bloat in early versions.
• Focused on structured outputs instead of generic long-form prompts.
• Built for repeatability, so users can start from reusable patterns, not blank pages.

Tech highlights

Architecture

• Next.js application for fast iteration and server/client integration.
• Supabase + PostgreSQL for persistence and simple operational overhead.
• API orchestration for AI generation and structured transformation.

Data model

Core entities were designed around:

• Project contexts
• Generated rule artifacts
• Reusable templates/patterns
• User-level organization of outputs

Product engineering decisions

• Kept schema and validation close to domain boundaries for safer evolution.
• Used incremental releases to validate assumptions quickly.
• Optimized for maintainability and low operational complexity.

Outcomes

Even at an evolving stage, the product delivered meaningful value:

• Better consistency in generated rule quality.
• Faster path from idea → usable AI instruction set.
• Reduced repeated setup work through reusable patterns.
• Stronger alignment between AI outputs and project conventions.

Challenges

1) Balancing flexibility with structure

Too much flexibility creates noise; too much structure feels rigid.
I solved this by offering guided inputs with enough freedom for project-specific constraints.

2) Keeping outputs useful, not verbose

Long outputs can look impressive but are hard to apply.
I iterated toward concise, operational outputs that can be used immediately.

3) Product scope control

It was tempting to add many adjacent features early.
I kept focus on one core value loop: context in → structured rules out.

What I’d improve next

• Add deeper team collaboration and versioning for rule sets.
• Expand export formats for more agent ecosystems.
• Add quality scoring/validation checks before export.
• Provide onboarding presets by stack (e.g., Next.js + Supabase + Stripe).

Final takeaway

RulesForAI demonstrates my product engineering style: identify a real workflow bottleneck, design a focused solution, and ship a practical system that improves speed, consistency, and developer confidence.