Descriptions:
Ravi Mehta, former CPO of Tinder and now a full-time vibe coder and product adviser, joins Peter Yang to make a detailed case for “context engineering” as the successor to traditional prompt engineering. The argument: the real skill is no longer writing a clever prompt, but designing all of the information — in a structured, 360-degree way — that an AI model needs to produce high-fidelity output. Mehta presents a layered framework covering spec/prompt context, data context, design context, and tool-augmented context via MCP servers.
The session includes a live walkthrough inside Claude Code, where Mehta demonstrates how separating a JSON data file from the prompt lets the prototyping tool infer its own UX decisions — producing genre detail pages, search filters, and grid layouts without explicit UI instructions. He also shows how swapping that JSON file (e.g., replacing jazz with psychedelic rock) instantly re-skins the entire prototype, and how an MCP server integration pulls album cover URLs from free APIs and images from Unsplash to make demos feel realistic enough to generate genuine customer research.
The deeper point is about prototype fidelity as a research tool: when a prototype looks and feels enough like a real product, users describe what they *are* doing rather than what they *would* do — dramatically improving signal quality. The talk is particularly valuable for product managers and builders who want to move beyond throwaway one-liners and use AI-generated prototypes as real decision-making artifacts in customer conversations and stakeholder reviews.
📺 Source: Peter Yang · Published May 03, 2026
🏷️ Format: Deep Dive
