Step 01
Synthesize Research in NotebookLM
Group the authorized research files by persona in NotebookLM, de-identify unnecessary participant details, and record the included source set, dates, and known sample limitations.
AdvancedProduct
How to Create Interactive AI Personas from Static Customer Research
Convert static customer research into clearly labeled synthetic personas whose responses stay grounded in cited source material, then use them for exploration without treating them as real customer validation.
Try this with your AI assistant
Download the skill for this workflow. Version 1.0.1.
Source episode · 24:00How I AI: Automate Recruiting and Build Interactive Personas with Michal Peled of HoneyBook
with Claire Vo
Michal loads HoneyBook’s customer research into NotebookLM, asks it to create source-grounded persona instructions with citations, tightens the prompts to fit Custom GPT limits, and tests five interactive personas.
Before you start
What you need
- Authorized customer-research files grouped by persona
- NotebookLM with a defined source set
- A Custom GPT workspace and instruction limits
- A research owner who can validate persona claims
- Representative test questions and known evidence
What you’ll make
A set of disclosed synthetic persona assistants that answer from the research, cite supporting material, state uncertainty, and help teams explore hypotheses.
Tools used
- ClaudeVisitClaude
Anthropic AI assistant
- NotebookLM
Google's AI-powered research assistant
- ChatGPTVisitChatGPT
OpenAI conversational AI
The workflow
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
4 steps
Step 02
Prompt NotebookLM to Act as a Prompt Engineer
Ask NotebookLM to produce one instruction draft per persona from the selected sources. Require citations for identity, needs, constraints, decision patterns, technology, journey, and communication preferences.
Example prompt
Create Custom GPT instructions for each research persona using only the selected sources. For every defining trait, include a source citation in the working notes. Cover context, goals, constraints, decision criteria, technology, journey, and communication preferences. Do not add a trait that is not stated or strongly implied; mark gaps as unknown.
Step 03
Refine and Add Guardrails
Tighten each draft to the platform limit and add behavioral boundaries: stay in persona, answer only from supported evidence, disclose uncertainty, avoid sensitive speculation, and never present the simulation as a real customer.
Example prompt
Compress these instructions to under [limit] characters without removing evidence-backed distinctions. The assistant must state when research is insufficient, avoid political, religious, racial, gender, health, and other sensitive inference unless essential and explicitly supported, and remind users that it is a synthetic research persona.
Step 04
Create and Test Your Custom GPTs
Create the Custom GPTs and test each with the same messaging, onboarding, pricing, and product questions. Compare answers with cited research, capture unsupported claims, and tell users when direct customer research is required.
Example prompt
Respond as this synthetic research persona using only its approved source material. Question: [question]. Give the answer, the research evidence or citation behind it, confidence, and what would need direct customer validation. Do not claim to be a real customer.
What good looks like
- Persona traits and answers trace back to the approved research set.
- The assistant refuses to invent beliefs when the source material is silent.
- Different personas produce meaningfully different, evidence-based responses.
- Teams know synthetic responses are prompts for research, not replacement customer evidence.
Build your next product with ChatPRD
Turn an idea into a PRD, user stories, and a plan.
Runbook notes
How to recover when the loop fails and where human judgment helps.
If it goes sideways
- Evidence from several segments is blended into one inconsistent persona
- Separate source sets, label overlaps, and require every defining trait to cite the intended segment.
- The persona fills research gaps with plausible stereotypes
- Instruct it to say the evidence is insufficient and ask the research owner to add or clarify sources.
- Teams quote the synthetic persona as if a customer said it
- Label every interface and export as synthetic, preserve citations, and route material decisions to direct customer validation.
- Research files or outputs expose personal or sensitive participant data
- Minimize and de-identify inputs, restrict access, and remove details not needed for the persona’s decision context.
Related workflows
intermediateDataMeta Muse
How to Use AI for Hobby Improvement and Data Analysis for Golf & Chess
Connect data from your hobbies (like golf scores or chess games) to an AI agent to get automated analysis, visual dashboards, and actionable coaching insights. Turn raw data into a story that helps you improve.
Open
beginnerPersonalMeta Muse
How to Create a Personalized Shopping Catalog from Your Instagram Saves Using AI
Turn your unstructured Instagram 'Saved' collection into a fun, personalized shopping catalog. This workflow uses an AI agent to organize your saves and present them in a nostalgic, engaging format like the SkyMall magazine.
Open
advancedContentMeta Muse
How to Revive an Old Blog or Digital Archive as a Personal AI Podcast
Resurrect your old digital content, like a forgotten blog from Blogger or Tumblr, by using an AI agent to find, consolidate, and transform it into a daily personal podcast. Reconnect with your past self in a new, listenable format.
Open
Start shipping better products.
Join 250,000+ product managers who use ChatPRD to write better docs, align teams faster, and build products users love.
Free to start · 14-day Pro trial · SOC 2 Type II
