The product problem
The platform needs to organize submitted content so people can find it through consistent categories and filters. That requires product decisions about useful information and discovery, alongside the application and data foundations that support them.
Choose labels for discovery value
I defined classification around a practical decision: would a label help someone distinguish relevant content from other results? Adding every plausible label could clutter the filters. I specified inclusion criteria, selection limits and rules for omitting inapplicable categories, combining AI interpretation with deterministic rules and examples with expected labels.
This trades broad labeling coverage for useful discovery. It is a design decision supported by specifications and components; end-to-end output evaluation remains unfinished. Inspect the classification decision →
My ownership and implementation
I own product strategy, requirements, architecture and development. APIs connect application behavior with stored data; identity and access define who can use protected capabilities, while subscription entitlements distinguish permitted features. My hands-on stack includes TypeScript, React, Next.js, Node.js and PostgreSQL. I have developed APIs, identity and access capabilities, and subscription entitlements, with cloud architecture and staging work across Azure and AWS.
Identity validation has included Cognito and Google. This case covers development and validation work.
Data decisions follow the user task
I developed taxonomies, normalization rules, classification requirements, prompt specifications and supporting components. The goal is consistent labels that help people distinguish relevant content. The classification case explains the criteria and the evaluation still required.
Connecting requirements to delivery
I built and refined a human-directed AI delivery workflow connecting requirements and GitHub Projects to bounded implementation, separate review, automated checks and human approval. I retain the decisions about scope, acceptance and integration.
The delivery case follows one requirement through a correction, a separate reviewer finding, tests and an approved revision. It provides a concrete example of how I inspect work produced with AI assistance.
Current evidence and next validation
The evidence here is hands-on platform development, an implemented delivery workflow, and classification design with supporting code. End-to-end classification validation is unfinished. The next classification step is to compare actual outputs with reviewed examples, including unnecessary and missing labels.
Quality and business outcomes remain to be evaluated.