Risks of autoregressive language models and the future of prompt engineering
Exploration and insight on topics that sit at the intersection of business and technology.
Four ways the party may be coming to an end
The AI product manager’s job isn’t over when the product is released. PMs need to remain engaged after deployment.
Identifying AI opportunities and setting appropriate goals are critical to AI success, and yet can be difficult to do in practice. Some reasons for this include lack of AI literacy, maturity, and many other factors.
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
Previous articles have gone through the basics of AI product management. Here we get to the meat: how do you bring a product to market?
How automation is likely to change professional software development.
Getting curious about the numbers attached to other people can help us to use data wisely—and to see others clearly.
Companies that succeed will protect, fight for, and empower their users
A product manager for AI does everything a traditional PM does, and much more.
Your models are only as good as your data.
Leaders highlight the importance of continuous improvement, applying lessons from technology processes, and striving for humility.
Immediate actions you can take to ensure business continues.