AI HRTech Product Specialist vs AI Product Requirements Specialist

B AI Product Requirements Specialist Only

• AI and LLM fundamentals - transformer architecture concepts, token economics, model capabilities and limitations
• Requirements engineering for probabilistic systems - writing acceptance criteria that handle non-deterministic outputs
• Prompt engineering literacy - understanding prompt design, few-shot patterns, system prompts, and guardrails
• User story mapping adapted for AI features - defining intent, context, expected AI behavior, and fallback paths
• Stakeholder elicitation and facilitation - running structured workshops to surface hidden assumptions about AI
• Data requirements specification - defining training data needs, RAG knowledge bases, and retrieval quality metrics
• API and integration specification - documenting LLM API contracts, rate limits, latency expectations, and error handling
• AI evaluation and benchmarking - specifying evaluation criteria including accuracy, hallucination rate, latency, and cost