AI Product Management
AI products do not fail only when they hallucinate. They fail when users lose control, recourse, purpose, or accountability. Product leaders need to design for agency before asking people to trust the system.
experiments
Sometimes you want a quick, dirty review of a product idea from people who you think are your target market. The standard playbook would have been: recruit 8-12 participants, schedule 45-minute calls across two weeks, transcribe everything, then spend another week pulling out themes. By the time you
As generative AI lowers the cost of creating drafts, prototypes, and written artifacts, the traditional bottlenecks of product development — execution, coordination, and production time — are losing their importance. When ideation and draft creation become inexpensive and abundant, product teams must shift their focus from generating options to evaluating them. Recent
experiments
A Lightweight, Developer-First Way to Test LLM-Powered Functions Over the past year, I’ve watched the LLM evaluation ecosystem get more complicated, not less. Teams are now building agent workflows, reasoning chains, translation systems, summarizers, RAG pipelines, and production LLM features. Yet one foundational problem remains surprisingly unsolved:
Large Language Models (LLMs) have transformed the way we approach artificial intelligence, enabling applications from chatbots to coding assistants. However, training these models effectively while managing costs and ensuring stability remains a challenge. Enter Group Relative Policy Optimization (GRPO), a reinforcement learning technique designed to optimize models without the overhead
Artificial intelligence can feel almost magical. It powers search, recommends movies, analyzes documents, and even assists in medical decisions. But there is a major challenge that is easy to overlook: bias. AI bias occurs when a system treats some groups unfairly, often in subtle ways. A simple example Imagine training
Hot take: The viral “95% of AI initiatives failed” headline doesn’t mean AI is overhyped or broken. It means most enterprises are still bad at choosing the right use cases, integrating tools into real workflows, and measuring actual impact. What the MIT study actually says In August 2025, MIT
AI product leadership: turning model capability into dependable, trusted products. The work starts after the demo