The framework that started simple has revealed unexpected complexity—and that's a good thing

The Specification Generator framework introduced in Solving AI Scope Creep: The Framework That Keeps Creativity From Running Wild has been applied across a multitude of feature builds now. This continued practice has revealed new subtleties and expanded its range of application.

What began as a two-phase approach has evolved into something more nuanced: three distinct modes of operation, each suited to different types of development challenges.

The Three-Mode Discovery

Further real-world application empirically revealed that AI-assisted specification generation works differently depending on the problem at hand:

1. Primary Approach (Formal Guidelines)

The original framework works exceptionally well for feature requests with well-defined business requirements. The structured clarification questions establish clear boundaries, then AI creativity explores comprehensively within those constraints.

Most effective for: Complex business logic, integration features, anything requiring regulatory compliance.

2. Few-Shot Templates

When building similar features repeatedly, showing the AI 2-3 examples of successful specifications produces better results than lengthy rule sets. The AI learns patterns from examples rather than following abstract guidelines.

Most effective for: UI components, similar feature variations, maintaining consistency across related functionality.

3. Freeform Exploration

Sometimes creative exploration must happen before boundaries can even be established. This mode allows pure AI creativity, often followed by specification tightening to channel discoveries into production-ready features.

Most effective for: Novel problems, design challenges, situations where the solution space is genuinely unclear.

Real Implementation Examples

Budget-Aware Strike Filtering

Challenge: Incorporating five separate business rules into Striketarget's derivatives analysis filtering mechanism—position limits, risk tolerances, volatility thresholds, and liquidity requirements.

Approach: Used the primary framework with formal guidelines to systematically work through each business rule, establish clear boundaries, then design a maintainable filtering system.

Outcome: Single-iteration delivery with immediate user adoption. The structured approach handled the complexity better than expected.

Learning: Complex business logic benefits from systematic boundary-setting rather than creative exploration.

Persistent Display CSS Grid

Challenge: Designing a grid layout for Striketarget's copilot interface that prevented text wrapping while maintaining accessibility standards.

Approach: Started with structured guidelines but found the requirements too fluid. Switched to freeform exploration to experiment with CSS Grid techniques, then formalized the successful approach.

Outcome: Clean, lightweight solution using advanced CSS Grid features that weren't initially considered.

Learning: UI/UX challenges sometimes require exploration before specifications can be properly defined.

Template Pattern Recognition

Discovery: After successfully building the first dashboard component using formal guidelines, subsequent similar components were developed much faster using the few-shot template approach—showing the AI the successful pattern rather than re-explaining the requirements.

Learning: Successful specifications become reusable templates, creating a library of proven approaches.

Institutionalized Improvements

Ongoing use has produced several practices now integrated into the framework:

Feature Tagging Strategy: Descriptive tags like budget-aware-strike-filtering document technical lineage and prevent versioning confusion.

Implementation Journey Documentation: Each feature now captures the full decision process—exploration methods, boundary decisions, solution rationale, and testing considerations—creating institutional knowledge.

Integrated Testing Requirements: Specifications mandate explicit testing updates tied to structural changes, ensuring new features don't destabilize existing functionality.

Mode Selection Guidance: The framework now includes criteria for choosing the appropriate mode based on problem characteristics and team context.

What This Means for Development Teams

Match the Mode to the Problem

Different types of challenges require different AI interaction patterns. Routine business logic benefits from structured approaches. Creative design challenges may require exploration before boundaries can be established.

Build a Template Library

Successful specifications become reusable patterns. The second time the AI tackles a similar problem, development can be significantly faster using proven approaches.

Document Decision Rationale

Specifications that capture why certain decisions were made help teams avoid repeating unsuccessful approaches and provide context for future modifications.

Expect Mode Switching

Many development sessions involve switching between modes as understanding evolves—starting structured, exploring when hitting obstacles, then returning to structured specification for implementation.

Updated Framework

The Specification Generator repository on GitHub has been updated to include these refinements:

Multi-mode operation guidance with selection criteria
Real examples across business logic and UI/UX domains
Template strategies for recurring patterns
Documentation standards for capturing implementation journeys
Testing integration requirements for production stability

Broader Implications

The most significant insight from continued implementation: AI-assisted specification generation isn't uniform. Sometimes structured questioning is sufficient. Sometimes examples teach better than rules. Sometimes exploration is necessary before requirements can even be properly defined.

This reflects how effective AI collaboration adapts to problem complexity rather than forcing every challenge into the same interaction pattern. Teams that recognize these different modes can choose the most effective approach for each situation.

Striketarget's options intelligence platform continues to serve as a testing ground for these approaches—complex derivatives analysis, trading interfaces, and risk management features all developed through disciplined AI specifications rather than unstructured creative generation.

The framework remains open-source, available for teams seeking to improve their AI-assisted development processes through lessons learned from production application.

The playground metaphor still holds: "First, build the fence. Then, explore every inch of the playground." But sometimes the most effective approach is building multiple playgrounds suited to different types of creative exploration.

Explore the updated framework →

Three Modes of AI Specification: Insights from Practice