Many, many more case studies available on request.

AI Coach


Defining the voice of an AI health coach and building the eval framework to keep it honest

As MyFitnessPal introduced AI Coach to ~700k Premium users, we faced a question the product had never had to answer before: what does "good" mean for an AI agent, giving advice in a category where language about food, weight, and calories can easily tip into judgment or harm?

I led the definition of the coach's core voice, establishing how it should balance empathy with efficiency, accuracy with personalization, and claimed helpfulness with actual agent ability.

Because our classic testing doesn’t solve for non-deterministic outputs, I partnered with our AI engineer to design our llm-as-judge eval system: a rubric scoring every response on safety, voice, and correct tool use (flow execution) across a set of key scenarios. This gives us objective signal on whether prompt changes are moving the coach in the right direction.

That framework is becoming the standard the team runs against every coach update before it ships, which shifted my role from "writes the prompt" to owning the quality bar for how the product speaks and behaves.

Streaks


Dissecting user motivation

Streaks began with a hypothesis about milestones: if we celebrated key days (3, 6, 10), users would be more likely to push toward the next one, driving our core metric of week-over-week food logging. For the first test, I worked with our business intelligence team to source social proof claims like "You're in the top 50% of users!" [LIFT: placeholder].

Frequency

That result opened a bigger question: how far could frequency go? We kept testing more celebration days until we were celebrating every single day from day 2–10. We found [that daily celebration outperformed milestones alone by X% / that we hadn't yet hit a ceiling on "too much"] — placeholder.

Timing

Alongside frequency, we tested timing: celebrating a user the instant they logged food and incremented their streak, versus waiting until they returned to the Today screen. The in-the-moment celebration won, even though it interrupts the user mid-task [LIFT: placeholder]. The principle this surfaced: celebrations are welcome distractions as long as they feel meaningful in the moment they happen.

Copy

With frequency and timing solved, I turned to copy. My hypothesis was that novel, varied copy each day would outperform repetition. But, it didn't. leaderboard-style social proof still won [LIFT: placeholder]. The takeaway reframed how I thought about streak copy going forward: users respond less to novelty than to feeling part of something larger than themselves.

That insight opened a second question: could belonging be built through identity, not just scale? I tested a day-1 streak for returning users (deliberately placed away from registration-day messaging clutter) leaning on identity: "Mary is back!" It won [LIFT: placeholder], confirming that identity-based framing can be as powerful as social proof.

Currently in testing is the inverse case: a day-1 streak for new users leaning on scale rather than identity: "You just joined 2.5 million users logging food today." [Results pending.]

Scores


My role: Lead Content Designer

Celebrations


My role: Lead Content Designer