H
@harshxharsh
H
A framework of automated and human-in-the-loop evaluations (e.g., code-based scripts, LLM-as-judge checks) that measures whether an AI product “actually works,” supporting rapid learning cycles and alignment with user and business goals.
AI Product Manager Glossary
Inference
The process of using a trained model to make predictions on new data. In the case of large language models, this means generating a response based on the input prompt. It’s what happens when you “ask” the model something.
The process of using a trained model to make predictions on new data. In the case of large language models, this means generating a response based on the input prompt. It’s what happens when you “ask” the model something.
AI Product Manager Glossary
Neural Networks
Computational models inspired by the human brain, consisting of layers of interconnected nodes ("neurons") that process data to learn patterns.
Computational models inspired by the human brain, consisting of layers of interconnected nodes ("neurons") that process data to learn patterns.
AI Product Manager Glossary
We're living in an era where AI can generate polished designs, code, and writing with a single prompt—but something crucial is being lost. Willem Van Lancker argues that “productive friction”—the struggle of learning through trial, error, and critique—is essential for developing true expertise and taste.
A student trying to sound knowledgeable without having done the reading, a political candidate saying things because they sound good to potential voters, and a dilet- tante trying to spin an interesting story: none of these people are trying to deceive, but they are also not trying to convey facts. To Frankfurt, they are bullshitting.
permanent sense of insecurity as fuel for excellence.
google.com • Inbox (6,480) - tyler.faught92@gmail.com - Gmail
PM risks —Value, Usability, Viability, Feasibility, and Go-to-market—for AI products .
google.com • Inbox (6,480) - tyler.faught92@gmail.com - Gmail
Bottom-Up vs. Top-Down Analysis
Two approaches to defining AI metrics. Bottom-up analysis identifies application-specific failure modes directly from the data. Top-down analysis applies generic metrics (like hallucination or toxicity) that may miss domain-specific nuances.
Two approaches to defining AI metrics. Bottom-up analysis identifies application-specific failure modes directly from the data. Top-down analysis applies generic metrics (like hallucination or toxicity) that may miss domain-specific nuances.
AI Product Manager Glossary
Write for one person: If you try to write for everyone, you write for no one. Focus on one person. It could be your loved one, your boss, or your peers. What do you want to share with that person?