Ravi Mehta, former CPO of Tinder and current AI instructor, argues that bad AI output is an input problem. In a live demo, he builds a production-quality music app using a three-layer context engineering framework: functional context defines what the app does, visual context controls how it looks, and data context shapes what it works with. Each layer is fed to the model separately before being combined into a single full-stack prompt.
The separation of the data layer is the core insight here. Most developers dump everything into one prompt and get generic results. Mehta shows why isolating data context, including building a custom MCP server inside Claude Code at the 15-minute mark, is what separates polished prototypes from AI slop. The live demo is the reason to watch this in full, not the explanation.
The episode also covers a real debate: whether PMs should prototype at all or push directly to production, and where PRDs still have a role in AI-native development. These sections start at 28:59 and 33:51. If you work in product and you are still treating AI as an autocomplete tool, this 40-minute video will reframe how you think about building.
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