AI Product Manager Super Workbench
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About this skill
Problem It Addresses
AI product managers often need to move between model capability, inference cost, retrieval quality, agent reliability, and compliance without a shared operating method. This workbench addresses that gap by organizing the AI product lifecycle into twelve stages, from opportunity sizing and data strategy to model selection, prompt and context engineering, RAG, agents, fine-tuning, evaluation, safety, operations, and commercialization. Rather than a list of scattered techniques, it structures each stage around inputs, process, methods, deliverables, quality standards, and PM checklists.
How It Works and Where It Stops
Key capabilities include:
- Role mapping: separates AI Builder PM, AI Experience PM, and AI-Enhanced PM to clarify ownership.
- Technical selection guidance: compares embeddings, vector stores, hybrid retrieval, and tools such as RAGAS, LangSmith, LangFuse, and Promptfoo.
- RAG and agent design patterns: covers ReAct, Plan-Execute, multi-agent coordination, GraphRAG, and Agentic RAG with tradeoffs.
- Quality and compliance gates: uses static, dynamic, and adversarial consensus checks, while flagging fast-changing topics like EU AI Act requirements and China generative-AI filing rules for live verification.
Boundary: the skill emphasizes traceability, reviewability, and freshness, but does not guarantee current correctness. Legal, safety, financial, and architecture decisions still require verification against the latest regulations, provider SLAs, and production data.
Use Cases
- Assess whether a customer-support transcription use case fits an LLM and produce data, cost, and risk recommendations.
- Design a knowledge-base RAG solution by comparing vector stores, chunking strategies, and evaluation metrics.
- Prepare a pre-launch AI quality evaluation plan covering hallucination, relevance, safety, and latency.
- Draft an EU AI Act compliance checklist for a product, identifying transparency and high-risk obligations.
Best For
- Product managers owning AI feature launch decisions who need to assess model fit, cost, and priority.
- Engineers or product managers building enterprise knowledge-base Q&A who need RAG component selection and evaluation standards.
- Compliance owners preparing AI products for international markets who need EU AI Act and transparency checks.
- Architecture leads delivering agent applications who need to compare ReAct, Plan-Execute, and multi-agent patterns.
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