By playing, you agree to uListen's Terms and Privacy Policy and YouTube's Terms of Service.
How I AIHow this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes
Summaries, chapters, transcripts, and FAQs on this page are made by uListen, not YouTube.
At a glance
WHAT IT’S REALLY ABOUT
Yelp AI PM prototypes by reverse-engineering “golden conversations” into interfaces
- The episode showcases a practical AI product management workflow that begins with writing (and generating) exemplar “golden conversations” to define the intended user experience before formal requirements.
- Priya uses Claude to generate and refine multi-scenario conversations—especially for a new Yelp Assistant feature that analyzes user-uploaded photos for home service requests—then extracts quality criteria and system-prompt direction from those examples.
- Next, she turns the conversations into an interactive prototype using Claude Artifacts, enabling realistic testing of response length, latency feel, and overall UX in a chat UI without setting up API keys.
- Finally, she explores front-end UI variations in Magic Patterns (including Inspiration mode) to iterate on entry points like “Start with a photo,” emphasizing rapid solution-space exploration and collaboration with design/engineering.
IDEAS WORTH REMEMBERING
5 ideasStart AI product design from the intended conversation, not the UI.
Priya’s “golden conversations” approach defines what success looks like in the user’s words first, then works backward into prompts, flows, and interface requirements—mirroring the end-user experience early.
Treat variability as a core AI product constraint and design for quality explicitly.
Because LLM outputs differ run-to-run, she focuses on methods to drive consistent quality: multiple examples, qualitative review, and eventually a rubric—aligned with the idea that “evals are the new PRD.”
Use multi-scenario, multimodal examples to uncover gaps and patterns fast.
Testing across diverse images (cracked porch, appliance error codes, wasp nest, bathroom renovation) reveals whether image recognition is robust and whether the conversation flow generalizes across categories.
Iterate conversations by giving targeted critique, then re-generating in bulk.
She provides concrete feedback (be more opinionated with recommendations, avoid asking about budget) and asks the model to rewrite all examples—accelerating convergence on a consistent voice and policy.
Interactive prototypes surface UX issues that text transcripts hide.
In an artifact/chat UI, message length, scrolling, and perceived waiting time (“three dots”) can make acceptable copy feel too long or slow—critical signals before engineering investment.
WORDS WORTH SAVING
5 quotesSo we start with golden conversations. What’s the experience that you’re trying to drive?
— Priya Badger
Write an example conversation… and you’re working backwards from that example conversation.
— Claire Vo
A lot of people talk about, like, evals are the new PRD. And this is, like, the very early step of getting… to the eval process.
— Priya Badger
Sometimes a response that looks fine when you have it in a doc feels really long when you see it in… the little chat bubble.
— Priya Badger
I think it’s helpful to actually think about the ways that AI is different than a human.
— Priya Badger
High quality AI-generated summary created from speaker-labeled transcript.
