How to build a Persona Feature Tester with Claude Code and ElevenLabs

· Source: Department of Product · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The Knowledge Series introduces a "Persona Feature Tester" tool designed for product teams to validate new features against user personas. This tool allows users to describe a feature or import it from Linear, then run it past multiple AI-powered user personas simultaneously. It provides summarized feedback, highlighting likes and dislikes, and enables conversational interaction with individual personas for deeper insights. The tool is built using real-world data sets to ensure persona realism and is not intended to replace direct user feedback but rather to augment assumption testing and multi-perspective analysis. Key technologies involved in its construction include Claude Design, Code, and Cowork, Cursor, and ElevenLabs, with a downloadable app and data examples provided for implementation.

Key takeaway

For AI Product Managers evaluating new feature concepts, the Persona Feature Tester offers a rapid, multi-perspective validation tool. You can quickly test assumptions against data-backed personas, gaining initial feedback before committing to extensive user research. This approach helps refine feature designs and identify potential issues early in the development cycle, accelerating your team's iteration speed.

Key insights

The Persona Feature Tester uses AI to validate new features against data-driven user personas.

Principles

Method

The method involves creating data-driven personas with Claude Cowork, building the app with Claude Design and Code, and integrating third-party APIs like Linear, ElevenLabs, and Anthropic.

In practice

Topics

Best for: AI Product Manager, Product Manager, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Department of Product.