Analyze multilingual feedback without flattening language or segment differences by preserving each original response, producing traceable translations and classifications, and turning the reviewed findings into a cited product brief.
Source episode · 04:00
How I AI: Gamma's 3-Step AI Workflow for Global Feedback, Art Direction, and Hiringwith Claire Vo
Zach uploads roughly 550 pieces of image editing feedback in many languages to ChatGPT Deep Research, answers clarifying questions, receives translations, classifications, themes, quotes, and citations, then turns the structured results into a Gamma presentation for product and design partners.
A row level analysis with original text, translation, category, sentiment or rating, evidence, confidence, and citations, plus a presentation that explains coverage, segment differences, limitations, and product implications.
OpenAI conversational AI
Step by step
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
3 steps
Export the permitted fields with a stable row ID, original response, language or locale, date, segment, feature, model, and rating. Remove unnecessary personal data and document the sampling frame before upload.
Ask Deep Research to translate and classify each row under a fixed rubric, then summarize themes and segment differences with citations and uncertainty.
Analyze this multilingual feedback for [research question]. First report rows, languages, dates, segments, missing fields, and exclusions. For each row preserve ID and original text, provide translation, category from [rubric], sentiment or rating, evidence, confidence, and NEEDS_REVIEW when ambiguous. Then summarize themes with counts, rates, exact citations, counterexamples, and segment differences. Do not invent quotations.
Review the row level output and key translations, then build a Gamma deck from a locked source outline. Include coverage, methods, findings, representative citations, differences by language or plan, limitations, decisions, and research questions without adding unsupported claims.
Create a product research presentation from this reviewed outline. Preserve every source row citation, qualifier, and unknown. Include coverage and method before findings, distinguish count from rate, show counterexamples, and do not add customer quotes or regional conclusions not in the outline.
Turn an idea into a PRD, user stories, and a plan.
After the steps
How to recover when the loop fails and where human judgment helps.
Recover
Keep building
Design a weekly meta-workflow that analyzes your interactions with your AI assistant to make it smarter. This system learns your writing style from edits, suggests new automations for repetitive tasks, and helps you filter valuable AI techniques from hype.
Create a daily AI workflow that summarizes yesterday's meetings, identifies urgent tasks, and proactively learns internal company terms it doesn't understand. This keeps your AI assistant's knowledge current and highly relevant to your work.
Build a recurring AI task that automatically gathers updates from your calendar, Slack, and notes to suggest weekly priorities and prepare you for upcoming meetings. Start every Monday with a clear, comprehensive plan without the manual effort.
Join 100,000+ product managers who use ChatPRD to write better docs, align teams faster, and build products users love.