From AI Quality Loops to Product Thinking — This Is How I Got Here

I started at Preplaced as an Associate Product Operations Manager. It wasn't a traditional PM role — but it taught me something that stuck. A 21% uplift in trial-to-paid conversion doesn't come from a deck. It comes from understanding exactly where a user journey breaks and designing the fix before anyone asks you to.

At Turing, I lead a team of 5–6 analysts running RLHF and SFT workflows for Fortune 500 AI clients. I've evaluated 5,000+ LLM interaction pairs — specifically looking for the failure modes that degrade model quality. Logic gaps. Hallucination patterns. The subtle ways a model sounds confident while being wrong. I was the feedback channel between annotation teams and delivery leads, turning error patterns into model improvement guidance. Most PM candidates have read about this process. I've run it.

That ground-level understanding of how AI fails is what I now bring to product work. When you've spent time inside RLHF feedback loops, you design differently. You ask different questions earlier. You don't treat hallucination as a UX problem — you treat it as an architecture decision. That shift in thinking is what I'm building on as I move into product.