# Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs

arXiv: https://arxiv.org/abs/2602.06197 · HTML: https://mlsanity.com/paper/2602.06197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 80 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 25 of 25)

- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Why Language Models Hallucinate](https://mlsanity.com/paper/2509.04664/) (2025)
- [Style Aligned Image Generation via Shared Attention](https://mlsanity.com/paper/2312.02133/) (2023)
- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [PERSONA: A Reproducible Testbed for Pluralistic Alignment](https://mlsanity.com/paper/2407.17387/) (2024)
- [Concept Decomposition for Visual Exploration and Inspiration](https://mlsanity.com/paper/2305.18203/) (2023)
- [CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI](https://mlsanity.com/paper/2312.11949/) (2023)
- [SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs](https://mlsanity.com/paper/2506.05598/) (2025)
- [Building Better AI Agents: A Provocation on the Utilisation of Persona in LLM-based Conversational Agents](https://mlsanity.com/paper/2407.11977/) (2024)
- [Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas](https://mlsanity.com/paper/2408.10937/) (2024)
- [Guided Profile Generation Improves Personalization with LLMs](https://mlsanity.com/paper/2409.13093/) (2024)
- [An Empathy-Based Sandbox Approach to Bridge the Privacy Gap among Attitudes, Goals, Knowledge, and Behaviors](https://mlsanity.com/paper/2309.14510/) (2023)
- [PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation](https://mlsanity.com/paper/2409.12538/) (2024)
- [DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas](https://mlsanity.com/paper/2511.07338/) (2025)
- [Leveraging Multimodal LLM for Inspirational User Interface Search](https://mlsanity.com/paper/2501.17799/) (2025)
- [Retrieve, Annotate, Evaluate, Repeat: Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation](https://mlsanity.com/paper/2409.11860/) (2024)
- [UXAgent: A System for Simulating Usability Testing of Web Design with LLM Agents](https://mlsanity.com/paper/2504.09407/) (2025)
- [Canvil: Designerly Adaptation for LLM-Powered User Experiences](https://mlsanity.com/paper/2401.09051/) (2024)
- [ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context](https://mlsanity.com/paper/2407.06866/) (2024)
- [Brickify: Enabling Expressive Design Intent Specification through Direct Manipulation on Design Tokens](https://mlsanity.com/paper/2502.21219/) (2025)
- [DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design](https://mlsanity.com/paper/2502.09867/) (2025)
- [Product Aesthetic Design: A Machine Learning Augmentation](https://mlsanity.com/paper/1907.07786/) (2019)
- [DesignFromX: Empowering Consumer-Driven Design Space Exploration through Feature Composition of Referenced Products](https://mlsanity.com/paper/2505.11666/) (2025)
- [Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use](https://mlsanity.com/paper/2404.13172/) (2024)

## Abstract (arXiv)

Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expensive to produce, and difficult to translate into actionable design features. As a result, personas risk serving as static reference points rather than tools that actively shape design outcomes. To address these challenges, we built Personagram, an interactive system powered by multimodal large language models (MLLMs) that helps designers explore detailed census-based personas, extract product features inferred from persona attributes, and recombine them for specific customer segments. In a study with 12 professional designers, we show that Personagram facilitates more actionable ideation workflows by structuring multimodal thinking from persona attributes to product design features, achieving higher engagement with personas, perceived transparency, and satisfaction compared to a chat-based baseline. We discuss implications of integrating AI-generated personas into product design workflows.