First, build the fence. Then, explore every inch of the playground.”

When building AI-powered trading software with a manifesto to reduce unforced errors, precision is paramount. A single misinterpreted requirement may not only lead to scope creep but also mean the difference between enforcing discipline — or introducing chaos.

The challenge? Our AI assistant was too good at generating ideas.

“The AI was solving ten problems we didn’t have while missing the one we actually needed to solve.”

The “Beautiful Disaster” Problem

A typical interaction looked like this:

Request: “Create a risk assessment component for the options workflow.”
AI: “Absolutely! I’ll build a comprehensive risk management system with real-time volatility tracking, Monte Carlo simulations, Greeks, portfolio stress testing, multi-feed integration, customizable alerts, machine learning pattern recognition…”
Clarification: “Stop. The requirement is a simple risk score display.”

The AI wasn’t going off the rails out of incompetence — it was over-delivering in the wrong directions.

Why Standard Scope Controls Failed

Rigid prompts → Followed the words, missed the intent
Checklists → Mechanical compliance without real understanding
Word limits → Shorter, but still unfocused
Multiple iterations → Clarification loops that slowed delivery

The AI’s instinct for comprehensiveness was a strength. The real challenge was to channel it.

The Breakthrough: “Build the Fence, Explore the Playground”

The shift came from a simple observation: creativity is maximized in a restricted space.

Phase 1: Build the Fence (Boundary Establishment)

Before generating solutions, the AI must define constraints:

What should this feature NOT do? (Essential in financial software)
Can the work be phased? (Keeps scope realistic)
How will it integrate? (Maintains data integrity)

Striketarget Example:

AI: “Before designing this risk component: – Is it for position sizing, or just metrics?
– Is it Phase 1 only?
– Which components must it connect with — and which must it avoid?”

Phase 2: Explore the Playground (Creative Abandon)

With boundaries locked in, the AI is encouraged to explore freely — but only inside the fence.

The result: focused, thorough specifications without scope creep.

Real Example: Options Analysis

Old approach: Haphazardly implement what amounts to a 15-page specification for a full-featured analysis platform — portfolio optimization, backtesting, social features, sentiment analysis — none required.

New approach: A 6-page deep dive into single-position analysis, Phase 1 only, fully integrated with the data pipeline. Same depth, zero creep.

Why This Works

Leverages AI strengths — Comprehensiveness becomes an asset.
Memorable metaphor — Easier to apply than rigid rules.
Fits the 80/20 reality — Most teams know the problem but not the boundaries.
Preserves quality — Detailed, focused specifications without dilution.

Results at Striketarget

75% reduction in redundant implementation work — fewer false starts and wasted cycles
Zero scope creep since adoption of the framework
Consistently sharper specifications — enabling smoother developer handoffs
Greater feature coherence across the suite, with each component solving exactly its intended problem

Every feature now addresses the right problem at the right depth. No more, no less.

Open-Sourcing the Framework

The complete framework has been published for public use. The repository includes:

A readable overview of the two-phase method with sample prompts and interaction flow
A guidelines document that operationalizes boundary-first specification work
Background on rationale and expected results, plus licensing details

Access the resource here:

github.com/striketarget/specification-generator

This framework is intended for engineering and product teams that want to reduce wasted cycles and keep AI-augmented development focused on the problems that truly matter.

Beyond Finance

The challenge isn’t making AI smarter — it’s making it a better partner.

In an options intelligence application suite, that’s about disciplined, virtuous execution. In any field, it’s about channeling creativity through constraints.

How to Start Using It

Implement the framework from the repository
Begin every request with boundary-setting questions
Let AI creativity run within those limits
Watch specifications sharpen — and build cycles accelerate

The takeaway: Good software starts with good specifications. Good specifications start with clear boundaries.

First, build the fence. Then, explore every inch of the playground.

Solving AI Scope Creep