Aakash GuptaGemini Gems Masterclass with the Creator at Google: 3 Gems You Must Build
Aakash Gupta and Lisa Huang on build Gemini Gems, ship AI agents, and future-proof PM careers.
In this episode of Aakash Gupta, featuring Aakash Gupta and Lisa Huang, Gemini Gems Masterclass with the Creator at Google: 3 Gems You Must Build explores build Gemini Gems, ship AI agents, and future-proof PM careers Gemini Gems are customizable Gemini instances that retain instructions and uploaded context so users stop re-prompting and can get consistently tailored outputs.
At a glance
WHAT IT’S REALLY ABOUT
Build Gemini Gems, ship AI agents, and future-proof PM careers
- Gemini Gems are customizable Gemini instances that retain instructions and uploaded context so users stop re-prompting and can get consistently tailored outputs.
- Lisa Huang recommends three must-have PM Gems—a writing clone, product strategy advisor, and user research synthesizer—mapped to core PM responsibilities of communication, strategy, and insight generation.
- Effective Gems require detailed instructions, the right contextual “knowledge” files, specialization by job-to-be-done, and ongoing iteration like a mini product.
- In high-stakes domains like finance, successful AI agents require hybrid systems (LLMs plus programmatic controls), strong domain workflows, robust evals, and human/LLM-judge quality loops.
- PM careers won’t be replaced by AI, but team structures and expectations will change toward “PMs as builders,” making AI tool fluency, prototyping, and technical depth increasingly mandatory.
IDEAS WORTH REMEMBERING
5 ideasTreat a Gem like a reusable context container, not a one-off prompt.
Gems exist because LLMs lack persistent context; by storing instructions and curated documents, you reduce repetitive prompting and get more reliable, “you-shaped” outputs over time.
Every PM should start with three Gems tied to core PM work.
A writing clone speeds stakeholder communication, a product strategy advisor supports decision-making with company/market context, and a user research synthesizer turns raw feedback into actionable insights.
High-performing Gems are built with specificity, examples, and scoped jobs.
Vague prompts (“help me write better”) underperform; detailed instructions plus relevant artifacts (PRDs, emails, research transcripts) and specialized Gems per task produce better consistency.
Iteration is not optional—quality comes from a feedback loop.
Lisa frames Gems as mini products: test outputs, refine instructions, update knowledge files as reality changes, and keep tightening until the Gem reliably matches your intent.
Gems differ from custom GPT positioning: prioritize productivity over monetization.
Google saw instructions as easily copyable and focused less on an “app store” model, instead optimizing for personal/team amplification and sharing within shared-context environments.
WORDS WORTH SAVING
5 quotesGemini Gems are custom versions of Gemini that you can create for your specific use case.
— Lisa Huang
If you aren't using Gemini Gems, you aren't getting the most out of Gemini.
— Lisa Huang
Let’s call them the writing clone, the product strategy advisor, and the user research synthesizer.
— Lisa Huang
Accuracy is not a nice-to-have, it’s a core part of our differentiation.
— Lisa Huang
I pay them for their product judgment.
— Lisa Huang
QUESTIONS ANSWERED IN THIS EPISODE
5 questionsFor each of the three “must-have” PM Gems, what are the minimum viable instruction blocks and the best 3–5 example artifacts to upload to get strong results fast?
Gemini Gems are customizable Gemini instances that retain instructions and uploaded context so users stop re-prompting and can get consistently tailored outputs.
When you say Gems “lack persistent memory” beyond instructions and files, what’s the practical maintenance routine (weekly/monthly) to keep a Gem current without it drifting?
Lisa Huang recommends three must-have PM Gems—a writing clone, product strategy advisor, and user research synthesizer—mapped to core PM responsibilities of communication, strategy, and insight generation.
If instructions are easily copied, what product moats (if any) could a Gem ecosystem have built—distribution, data, workflow integrations, or something else?
Effective Gems require detailed instructions, the right contextual “knowledge” files, specialization by job-to-be-done, and ongoing iteration like a mini product.
In the Ray-Ban assistant work, what were the highest-impact product decisions that were constrained by battery, latency, and privacy, and how did you trade them off?
In high-stakes domains like finance, successful AI agents require hybrid systems (LLMs plus programmatic controls), strong domain workflows, robust evals, and human/LLM-judge quality loops.
For finance agents like JAX, what specific failure modes show up most often (math errors, tool-calling, policy, user intent), and what mitigations worked best?
PM careers won’t be replaced by AI, but team structures and expectations will change toward “PMs as builders,” making AI tool fluency, prototyping, and technical depth increasingly mandatory.
Chapter Breakdown
Why Gemini Gems matter: personalized context for better outputs
Aakash frames Gemini’s rise and tees up why using Gemini Gems unlocks meaningfully better results than prompting a generic LLM each time. Lisa explains the core problem: LLMs are powerful but lack persistent context, forcing repeated re-explanations.
The 3 must-have Gems for product managers
Lisa outlines three core Gems every PM should create to cover the most common PM workstreams. Each Gem corresponds to a major PM responsibility: communication, strategic decision-making, and research synthesis.
How to build a Gem: instructions, knowledge, and iteration loop
Lisa breaks down the simple creation flow inside Gemini and what actually drives quality. The “build” process is framed like creating a mini-product: specify behavior clearly, ground it in the right documents, then test and refine.
Live demo: building a Product Strategy Buddy Gem
Lisa screenshares and constructs a product strategy Gem in real time, showing how it’s configured and validated. She demonstrates adding company context (strategy, roadmap, competitive teardown) and running a first test prompt to evaluate output quality.
Team sharing and organizational use cases
The conversation shifts from individual productivity to team leverage. Lisa explains that Gems can be shared across colleagues, especially when a team has common context and repeatable workflows.
Origin story: why Google built Gemini Gems
Lisa recounts the 2023 product insight behind Gems: users struggled to discover and repeatedly access Gemini’s capabilities. Personas and customizable assistants were a way to improve discoverability, fit mental models, and enable sharing.
Gemini Gems vs ChatGPT custom GPTs: different product thesis
Aakash asks how Gems compare to OpenAI’s custom GPTs and whether Gemini copied the concept. Lisa explains timing overlap and highlights a strategic divergence: Gemini focused on productivity rather than an “app store” monetization ecosystem.
PM Gem portfolio thinking: mapping Gems to core PM skill areas
They discuss how to decide what to build beyond the three starter Gems. Lisa proposes breaking PM work into strategy, execution, communication, and research signals—then creating multiple specialized Gems across those buckets.
Career philosophy and lessons from Apple, Meta, and Google
Lisa transitions into career advice, emphasizing curiosity as the guiding principle rather than a rigid plan. She compares product cultures across Apple, Meta, and Google and extracts what she carried forward from each environment.
Building the Meta Ray-Ban glasses AI assistant: constraints and zero-to-one reality
Lisa details her work on the first-gen assistant for Ray-Ban Stories (2019–2021), including skepticism she faced and how the project got funded. She highlights the unique challenges of wearable AI: privacy, hardware constraints, and partnership complexity.
Cloud vs on-device AI for wearables and how to build in fast-changing tech
They explore architectural tradeoffs for AR AI and what’s likely to change. Lisa predicts a wave toward on-device AI driven by privacy and practicality, and advises PMs to balance deep tech understanding with user value and rapid iteration.
Xero’s JAX financial super-agent: automating workflows with domain + data advantage
Lisa introduces Xero and explains JAX as an umbrella initiative to map financial workflows and automate jobs-to-be-done with agents. She argues Xero’s differentiation comes from deep workflow knowledge and rich transaction-level data across SMBs.
Reliability in high-stakes agents: hybrid systems, expert annotation, and quality flywheels
Aakash probes hard lessons in building agents where accuracy is critical (finance). Lisa explains why raw LLMs aren’t enough and how Xero uses a hybrid approach with programmatic controls, evaluations, and domain expert oversight.
Measuring an AI agent: quality → engagement → business impact
Lisa provides a measurement framework that ladders from technical correctness to adoption and finally monetization. She describes how eval criteria vary by use case and why quality tracking must be ongoing as the product evolves.
Will AI replace PMs? Role compression, PM-as-builder, and how to break into AI PM
Lisa argues PMs won’t be replaced because judgment and taste remain essential, but team structures and ratios will compress. She advises PMs to become builders—using AI to prototype, design, and even code—and shares a practical roadmap for breaking into AI PM roles.
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