PRD Decision Engine
An AI-powered system that transforms product requirements into structured decision frameworks, enabling faster and more consistent product decisions.
Overview
The PRD Decision Engine is an AI-powered system designed to bridge the gap between raw product requirements and executable decision frameworks. Product teams often spend hours turning messy requirements documents into coherent specs, and even more time aligning on how to make decisions during implementation. This system applies structure at the point of creation, so that decisions are baked in from the start rather than argued over later.
The core insight is that PRDs are fundamentally decision documents—they encode choices about scope, priority, success criteria, and trade-offs. By treating them as such and using AI to extract and formalise those decisions, we can reduce ambiguity, speed up alignment, and create reusable patterns that compound across projects.
What I Built
I built a pipeline that takes natural-language PRDs as input and produces structured decision frameworks as output. The system parses requirements, identifies implicit decisions, surfaces trade-offs, and outputs a structured format that teams can use for reviews, prioritisation, and implementation planning. It integrates with LLMs to handle varied input formats and domain-specific language.
The architecture separates parsing (extract structure from text), reasoning (infer decisions and dependencies), and output formatting (produce frameworks in different representations). This modular design allows the system to evolve as product practices change and as we learn which decision structures work best for different contexts. The system also learns from feedback: when teams mark decisions as good or bad, that signal can improve future extractions.
Why It Matters
Product velocity often stalls not because of technical constraints but because of decision friction. Teams spend cycles rehashing the same questions, revisiting scope, and realigning on priorities. A system that makes decisions explicit and structured reduces that friction. It turns "what did we agree on?" into something you can query and trace.
On a larger scale, this kind of tool represents a shift toward AI-native product work: using AI to handle the structural, repetitive parts of product thinking so humans can focus on judgment, creativity, and edge cases. For organisations that scale product teams, having consistent decision frameworks also makes it easier to onboard new people and maintain quality as complexity grows.