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.
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