AI B2C Product Specialist vs AI Product Manager

B AI Product Manager Only

• AI and machine learning fundamentals - understanding model types, training pipelines, inference trade-offs, and evaluation metrics
• LLM ecosystem fluency - transformer architectures, prompt design patterns, RAG architectures, fine-tuning strategies, and agent frameworks
• Product strategy and roadmap ownership - defining AI product vision, prioritizing with RICE or ICE frameworks adapted for AI uncertainty
• Data-driven decision making - designing A/B tests for AI features, interpreting confidence intervals, and measuring AI-specific KPIs
• Prompt engineering and AI prototyping - building functional prototypes with APIs and no-code tools to validate concepts before full engineering
• AI ethics and responsible deployment - bias auditing, fairness metrics, transparency requirements, and regulatory awareness
• Stakeholder communication - translating model behavior, limitations, and probabilistic outputs for executive and non-technical audiences
• Technical writing - authoring detailed PRDs with AI-specific sections covering data requirements, fallback strategies, and human-in-the-loop designs