Debra Capadona
Senior TPM / program delivery leader for complex, regulated environments — focused on judgment, composure, and shipping systems that hold up under scrutiny.
Working System Overview
To demonstrate how I use AI as a force multiplier, I selected a moderately complex, real-world use case and built a complete, end-to-end system. The goal was not novelty, but execution: clear architecture, disciplined data handling, and prompt design that produces reliable, repeatable outputs.
If you only have a minute: open the system, scan the outputs, then come back for the operating model.
Executive Profile
This profile summarizes delivery posture and leadership approach; detailed experience, roles, and timeline are in the resume. The goal here is simple: make it easy to evaluate judgment, operating model, and evidence.
Operating Model
A repeatable approach for complex delivery — designed to avoid "confidently wrong" outcomes.
How the System Was Built (AI-Enabled, Human-Governed)
The system is an intentionally bounded MVP: repeatable runs, explainable outputs, and explicit constraints. AI accelerates implementation and documentation — decision authority stays human.
GenAI & Prompting Pages
These pages document how GenAI was used in practice: prompts as interfaces, structured outputs, and verification loops. They're intentionally written like build notes — so you can see how decisions were made and why the system is repeatable.
Where LLMs accelerated design, implementation, debugging, and iteration — while architecture and judgment stayed human-owned.
→ OpenReusable patterns: constraints, output contracts, evaluation criteria, and iteration loops that keep outputs reliable.
→ OpenA full prompt example showing structure, definitions, and a strict JSON output contract.
→ OpenEvidence
The live system is the working artifact. This page exists to make the method legible: how ambiguity becomes a plan, how risk is managed, and how delivery stays controlled.