AI Product Manager 101 is an 🏷️#article framing the role of an AI-first 📝Product Manager as being focused on aligning user needs, business goals, and model capabilities to ship reliable AI features end to end. It emphasizes prompts as the user-facing interface to 📝Large Language Model (LLM)s—defining role, task, constraints, evidence, and outputs—with quality guidance drawn from 📝OpenAI and 📝Anthropic. It recommends evaluation via golden datasets and 📝LLM-as-a-Judge with blinding and audits, alongside security tests informed by the OWASP Top 10 for LLMs. It distinguishes context engineering from “📝Vibe Coding,” cites agentic workflows (e.g., 📝Model Context Protocol (MCP) and lightweight Agents.md), and suggests tool choices such as GPT-5, 📝Claude Code, 📝Cursor, 📝Lovable, and 📝Vercel’s v0 to match task requirements.

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Contexts

• 🏷️#adaline-labs (See: 📝Adeline Labs)