Five Practical Skills Every AI Product Manager Uses Daily

Aman Khan republished a post adapted from a conversation with Aakash Gupta outlining five practical skills an AI product manager (AI PM) uses daily: rapid AI prototyping, observability (LLM tracing), structured AI evaluations, technical intuition for choosing between prompt engineering/RAG/fine-tuning, and close PM-engineer collaboration. Khan emphasizes hands-on practice—coding simple prototypes with tools like Cursor, tracing AI interactions with APMs, creating small eval sets to measure qualities (accuracy, conciseness, friendliness), and working directly with engineers on labeling and model reviews. He provides a four-week transition plan (tool setup, observation, measurement, collaboration) and urges PMs to embrace being beginners, iterate quickly, and treat eval metrics as new product north stars. Primary keywords: AI product management, AI PM skills, prototyping, observability, AI evals. Secondary/semantic keywords: LLM tracing, RAG, prompt engineering, fine-tuning, model evaluation, PM-engineer partnership. This guidance is practical for product teams adopting LLMs and agents and is aimed at accelerating effective AI product development through measurable QA and closer cross-functional workflows.