Project Snapshot
5+ teams
Partnered across model training, UX research, and quality.
Edge-case focus
Crafted ambiguous prompts to stress-test reasoning.
Feedback loops
Structured scoring + narrative rationale for every verdict.
Why it matters
Evaluation is the UX lab for AI—my work ensures models feel transparent, safe, and genuinely useful before they reach real people.
Context & Problem
Language models are trained on vast amounts of data, but understanding what makes a response truly useful—versus superficially plausible—requires human judgment. Models can generate text that sounds confident but is factually incorrect, helpful-sounding but misaligned with the request, or technically correct yet hard to act on.
The challenge is evaluating responses not just for correctness, but for clarity, reasoning transparency, safety, and genuine helpfulness. This demands a UX mindset: interpreting user intent, predicting edge cases, and articulating why a response succeeds or fails along multiple dimensions.
My Role
I operate as a human-in-the-loop evaluator, blending UX research rigor with technical curiosity. Day to day, that includes:
- Interpreting complex prompts and latent requirements before models respond
- Scoring and annotating outputs for accuracy, reasoning clarity, tone, and safety
- Comparing multiple responses to articulate trade-offs and recommend a winner
- Designing adversarial prompts that expose brittle reasoning or lack of guardrails
- Translating observations into structured rubric updates and product feedback
Process
Decode intent
Many prompts are intentionally ambiguous. I highlight implicit needs, risks, and success criteria before judging the response so my scoring reflects real-world users.
Stress-test reasoning
I look for evidence of structured reasoning—step-by-step logic, sourcing, and acknowledgement of uncertainty—rather than surface-level confidence.
Evaluate trust & safety
I flag advice that could mislead, escalate harm, or overstep policy boundaries, then document mitigation ideas for model trainers.
Close the loop
Detailed rationales feed back into product and research teams so guidelines, heuristics, and future model checkpoints reflect human expectations.
Outcomes & Learnings
- Trustworthy AI is about transparency, not just accuracy—models must show their work to earn confidence.
- Edge cases reveal weaknesses faster than happy paths, so adversarial thinking is now core to my UX practice.
- Writing great feedback is a design exercise: I distill messy intuition into structured, reusable heuristics teams can ship.
- Balancing throughput with depth is a craft—pattern spotting without losing nuance is what makes this work impactful.
Reflection
This role has given me a front-row seat to how AI systems are shaped by human feedback. It has reinforced my ability to think like both a user and a system designer—anticipating where things go wrong, understanding what “good” really means, and translating observations into actionable guidance.
Ultimately, great AI UX is about informed trust. I bring that lens to every project, whether I am evaluating models, designing workflows, or exploring speculative concepts.