How I AIHow this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes
CHAPTERS
Meet Priya (Yelp AI PM) and the “golden conversations” idea
Claire introduces Priya Mathew Badger, a PM at Yelp, and tees up her distinctive workflow for building AI products. The core concept: start from “golden conversations” (ideal user-assistant exchanges) and work backward into requirements, prompts, and prototypes.
Why AI product management is different: UI + system behavior + quality variability
Priya breaks down what changes when products are powered by AI: the user interface still matters, but so do system prompts and backend behavior. A major challenge is that AI outputs vary run-to-run, so quality and consistency become central product problems.
Case study setup: Adding photo upload intelligence to Yelp Assistant
Priya explains Yelp Assistant’s service-request flow (describe a need → gather details → match to pros → get quotes). The new capability: users upload photos, and the assistant should interpret the image and adapt the conversation accordingly.
Starting point: Draft the ideal conversation flow in Claude
Priya demonstrates prompting Claude to generate a full sample conversation, including image analysis and suggested replies. She emphasizes specifying output format (roles, continuous dialogue) to reduce iteration and produce usable artifacts.
Why “example conversation first” works (and how teams use it)
Claire highlights the novelty: instead of beginning with UI mockups or PRDs, Priya begins with a realistic transcript of usage. Priya explains this mirrors Yelp’s internal LLM playbook—define the intended experience, then derive requirements and implementation details.
Testing across scenarios: multiple images to surface patterns and evaluation criteria
Priya scales testing by feeding Claude many images (appliance error codes, pests, renovation photos) to generate multiple conversation examples. She uses these to identify patterns, define what “good” means, and begin shaping an evaluation rubric.
Refining the “golden conversations” with feedback loops
Priya shows how to iteratively improve the conversations by giving targeted feedback (be more opinionated, avoid budget questions). Claude rewrites all scenarios, accelerating convergence on the desired tone, guidance level, and structure.
Demo: Turn conversations into an interactive prototype with Claude Artifacts
Priya uses Claude Artifacts to generate a working chat app that calls Claude’s model, including a draft system prompt derived from the example conversations. She also supplies Yelp Assistant screenshots to steer the prototype UI toward realistic styling.
Why interactive prototyping matters: bubble length, latency, and “feel”
Priya tests the Artifact and explains why it’s more revealing than reading a transcript in a doc. Message length, waiting indicators, and pacing can change perceived quality—so simulating the real interface can expose issues early.
From conversation design to UI exploration with Magic Patterns
Priya pivots to the user interface layer: entry points, prompts, and flows that communicate assistant capabilities. She demos Magic Patterns to visually explore UI changes, like adding a “Start with a photo” prompt suggestion.
Exploring variants fast: Magic Patterns “Inspiration mode” + iteration realities
Using Inspiration mode, Priya generates multiple differentiated design options for a more guided “start with photo” flow. They also hit a prototyping error (React version issue), illustrating the practical reality: AI tools accelerate ideation but still require debugging and judgment.
Applying the workflow beyond work: newsletters, parenting helper, and game timers
In a lightning-round tour, Priya shares personal uses of AI prototyping: auto-summarizing Slack threads into a newsletter via a Claude project, building a “Parent Pal” artifact, and creating a Settlers of Catan timer/leaderboard app using Lovable.
When AI won’t cooperate: reset context and “treat it like a system, not a human”
Priya recommends stepping back from conversational habits and diagnosing AI limitations, especially context-window issues. Practical fixes include forking/remixing prototypes to reset context and summarizing long chats into a fresh session.
Wrap-up: where to follow Priya and final takeaways
They close by reiterating the non-linear, fast iteration enabled by AI: start with end-user experience, iterate cheaply, and bring teams along with interactive prototypes. Priya shares where to find her work and tips.
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