Hi guys, I just wrote a new blog explaining the role of AI PM and why it is a core product role. AI PMs connect user needs, business goals, and model capabilities together. This allows them to then ship features safely, measurably, and with confident, not just “demo wow.” I have share the three lessons: 1. Prompt Engineering for AI PMs 2. Context Engineering and Vibe Coding 3. The AI PM’s Coding Toolkit Here’s what the lessons cover: • Prompt as interface: How to write prompts like product specs—role, task, constraints, evidence, success criteria. • Real evals: Golden datasets, LLM-as-a-judge with safeguards, latency and token budgets, plus prompt-injection checks. • Context engineering vs. vibe coding: When to build reliable, grounded systems vs. rapid NL prototyping. • Tooling guide: GPT-5, Claude Code, Cursor, Windsurf, Lovable, and Vercel v0—what each is best at, independently. • KPIs that matter: Task success, p95 latency, cost per action, and safety/regulatory fit. Why it matters: this playbook helps teams deliver trustworthy AI features that move real metrics. Read the blog: https://go.adaline.ai/6yeSMaJ Would love your thoughts or questions in this thread!