Discover & define:

- Talk to users, understand real workflows, identify pain points, and convert ambiguous asks into clear problem statements and requirements.

Shape solutions:

- Write crisp PRDs, user stories, acceptance criteria, workflows, edge cases, and MVP scopes.

- Partner closely with UI/UX.

Prioritize:

- Maintain the backlog and recommend priorities using user impact, business impact, effort, urgency, and strategic value.

Drive delivery:

- Work with engineering through development, clarify decisions, review builds, test workflows, and ensure release readiness.

Own adoption:

- Plan launches, demos, training, release communication, and follow-up improvements to drive user adoption.

Measure & improve:

- Track usage and adoption, identify drop-offs or friction, and translate data and feedback into product improvements.

AI-first product thinking:

- Use AI tools and LLM APIs throughout discovery and delivery to test ideas quickly, improve workflows, and identify high-value AI product opportunities.

AI PRDs:

- Define expected model behavior, inputs/outputs, guardrails, fallbacks, edge cases, success metrics, and human-in-the-loop requirements for AI features.

AI prototyping:

- Build fast proofs of concept using LLMs, agents, APIs, no-code/low-code tools, and AI-assisted coding before committing significant engineering effort.

AI evals:

- Create representative test cases and evaluation criteria for quality, relevance, reliability, latency, cost, and failure modes; use results to iterate before and after launch.