AI EVALUATION.

Quality is a product system, not a final QA step.

My AI evaluation work sits between product, engineering, and model behavior. The objective is not simply to decide whether an output is “right.” It is to define useful success criteria, stress the system, identify patterns in failure, and feed those findings back into product decisions.

How I approach evaluation

• Translate product requirements into explicit evaluation criteria and testable behaviors.
• Create representative, adversarial, and edge-case prompts that expose weak spots.
• Separate factual correctness from instruction following, reasoning quality, safety, usability, and consistency.
• Analyze repeated failures to distinguish prompt issues, tool-use issues, model limitations, and workflow design problems.
• Use coding and agentic tools to accelerate test generation, comparison, and result analysis.

Red teaming mindset

I treat model testing as a search problem: find the boundary where the system stops behaving as intended. That means varying ambiguity, conflicting instructions, missing context, unusual user goals, long-horizon workflows, and tool interactions rather than only testing happy paths.

Inspect the implementation

Proofrail makes this approach concrete in a provider-neutral Python evaluation prototype: a 21-surface preflight, Oracle and negative controls, repeated-trial scoring, and criterion-level Markdown and JSON evidence.

The public examples are synthetic. They demonstrate configured local checks, not production model performance or enterprise readiness. The case study includes reproduction steps and limitations. Bravado shows a complementary agent workflow for source-grounded product storytelling and reviewed media exports. No confidential Handshake or Outlier task content is included.

What this demonstrates

This work reflects a broader strength in my portfolio: I can operate between technical implementation and product judgment. I am comfortable testing systems deeply, communicating failure patterns clearly, and turning those findings into changes engineers and product teams can act on.