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LAB / AI-ASSISTED DEVELOPMENT

Building this portfolio like a product.

I designed and built this site using AI-assisted development. I used product strategy, art direction, iterative prototyping and QA to shorten the distance between an idea and something I could inspect, test and improve.

Intent problem + constraints Artifact working prototype Evidence critique + QA Revision iterate + refine Intent problem + constraints Artifact working prototype Evidence critique + QA Revision iterate + refine
Product
princemwenga.com
My role
Product lead + builder
Focus
AI-assisted product development
Outcome
Shipped responsive portfolio
7 case studies
one shared narrative and visual system
Desktop + mobile
reviewed as complete experiences, not isolated screens
Human judgment
kept responsible for facts, decisions and final quality

A résumé can show what shipped. It doesn't show how I think nearly as well: what I noticed, which trade-offs mattered, how I worked with technical constraints, and why one path was chosen over another.

I wanted a portfolio that could do both jobs: let a recruiter understand the signal quickly, while giving a product leader or engineer enough depth to see the reasoning underneath the outcome.

The constraint was that the site also had to stay coherent as the content grew. Seven case studies could not become seven unrelated microsites.

Design for the skim. Reward the deeper read.

The primary audience was hiring teams: recruiters, product leaders and engineers evaluating whether the work was credible and whether I could operate at senior scope.

That led to a simple design rule: every case needed a fast path through problem → decision → trade-off → outcome, with enough evidence underneath for someone who wanted to keep reading.

Audience: Recruiters · PM leaders · engineers
Job: Prove scope · judgment · trade-offs · outcomes
Constraint: One system across seven distinct stories

KEY DECISION

Don't use AI to skip thinking. Use it to shorten the build–learn loop.

OPTION A
Generate pages quickly.
Optimize for output first and hope the system becomes coherent later.
OPTION B · CHOSEN
Define the product system first.
Use AI to accelerate prototyping, implementation and iteration inside clear constraints.

AI was most useful when it compressed the distance between a decision and something tangible enough to critique. The loop stayed familiar; the handoff time inside the loop changed.

01Define the problem
02Set constraints
03Prototype
04Critique
05Verify
06Ship

The tool accelerated execution. Accountability stayed human.

AI ACCELERATED
Prototype variations
Front-end implementation
Copy and visual critique
Repetitive implementation work
QA assistance and code inspection
I REMAINED ACCOUNTABLE FOR
Product strategy and information architecture
Case-study narrative and evidence
Art direction and visual-system decisions
Factual accuracy and confidentiality
Final implementation and quality decisions

The biggest risk with fast generation is accepting something because it looks finished. I treated every output as a draft until it survived the same questions I would ask of a product artifact.

CHECKWHY IT MATTEREDWHAT I LOOKED FOR
Source of truthPrevented polished copy from outrunning the evidenceFacts, qualifiers, shipped vs. designed work, confidentiality
Design systemKept seven cases feeling like one productShared typography, spacing, component grammar and visual hierarchy
Responsive QADesktop approval does not guarantee a usable mobile storyOverflow, stacking, readability, scroll rhythm, console and interaction issues

WHAT CHANGED

Closer to the artifact
I could turn a product opinion into something inspectable without waiting for a full handoff cycle.
Tighter critique loops
Design and copy decisions could be tested against the whole experience instead of discussed only in the abstract.
More deliberate QA
Faster building made source-of-truth discipline and final verification more important, not less.
AI shouldn't replace product judgment. It should shorten the distance between an idea, an artifact and what you can learn from it.

The useful shift for me was not “I can generate a website.” It was realizing how much closer a product person can now stay to the thing being built. That still means respecting the expertise of designers and engineers and being clear about where the tool ends and accountability begins.

THE POINT OF THE EXPERIMENT

Get closer to
the thing being built.

AI accelerated the loop. Product judgment still decided what deserved to ship.

See the product work →