🏫
Obvious University
Website
  • 👋Welcome to Obvious University!
  • Strategy
    • Sprints
      • 1️⃣Map
      • 2️⃣Sketch
      • 3️⃣Decide
      • 4️⃣Prototype
      • 5️⃣Test
    • Benchmarking
    • Research
      • 1️⃣Research guide
      • 2️⃣How to recruit users
      • 3️⃣How to conduct an interview well
      • 4️⃣How to take notes
      • 5️⃣How to prep for remote research
      • 6️⃣How to throw a watch party
      • 7️⃣How to create artefacts
  • Working with Features
    • Building with AI
      • 1️⃣Understand the tech
      • 2️⃣Map your product
      • 3️⃣Build a proof of concept
      • 4️⃣LLM Inputs
      • 5️⃣LLM Responses
    • Building Help and Support
      • 1️⃣How to scope a support experience
      • 2️⃣How to design discovery for support
      • 3️⃣How to design a support centre
      • 4️⃣How to write good support articles
  • Product Design
    • Microcopy
      • 1️⃣How to write well
      • 2️⃣How to write phrases
      • 3️⃣How to write messages
      • 4️⃣How to create a voice
    • Typography
      • 1️⃣How to compose type
      • 2️⃣How to create a type scale
      • 3️⃣How to pick typefaces
      • 4️⃣How to pair typefaces
    • Design System
      • 1️⃣Introduction to design systems
      • 2️⃣How to audit a design system
      • 3️⃣How to run a design system pilot
      • 4️⃣How to set up a design foundation
      • 5️⃣How to build components
      • 6️⃣How to document a design system
      • 7️⃣How to enable adoption and govern a design system
    • Mobile Engineering
      • 1️⃣Trunk based development
      • 2️⃣Agile development terminology
      • 3️⃣Git commit messages
      • 4️⃣Code review and pull requests
      • 5️⃣Readings
  • Delivery
    • Project Management
    • Collaboration
  • Hiring and Growth
    • Growth
      • 1️⃣Design growth framework
      • 2️⃣How to give ongoing feedback
      • 3️⃣How to check-in every quarter
      • 4️⃣How to address underperformance
      • 5️⃣FAQs
    • Hiring and careers
      • 1️⃣The Hiring Process
      • 2️⃣Diverse and Inclusive Hiring
  • PEOPLE EXPERIENCE
    • Benefits and Perks
      • 1️⃣Paid time off
      • 2️⃣Insurance and healthcare
      • 3️⃣Continuing education
      • 4️⃣Speaking at conferences
    • Starting at Obvious
      • 1️⃣Introducing Obvious
      • 2️⃣Set up your workspace
      • 3️⃣Onboarding
      • 4️⃣Finances
      • 5️⃣Code of Conduct
    • Employment policies
      • 1️⃣Equal opportunity employment
      • 2️⃣At-will employment
      • 3️⃣Employee records and privacy
      • 4️⃣Prevention of sexual harassment
      • 5️⃣Drugs and alcohol
      • 6️⃣Fraternisation
      • 7️⃣Non-compete and non-solicitation
      • 8️⃣Non-disclosure
Powered by GitBook
On this page
  • 👋 Introduction
  • 1️⃣ Pick the right problem
  • Solution as a feature
  • Solution as a platform
  • Solution as a person
  1. Working with Features
  2. Building with AI

Map your product

PreviousUnderstand the techNextBuild a proof of concept

Last updated 1 year ago

Assumed audience: You’re a product manager working on an LLM powered feature. You’re familiar with how LLMs work. Use this as a starting point to think about the problems that LLMs are best suited to solve, and to start experimenting with these solutions.

👋 Introduction

The traditional process of identifying flows and steps is still important. Pick a core product flow, or job to be done and identify specific places where a tiny reasoning engine will improve experiences.


1️⃣ Pick the right problem

To build with LLMs, the tech stack will need to have independent modules for sources, vector indexes, picking prompts, creating API calls, and fetching summaries. Building this infra can be expensive, so it’s really important to identify specific high-ROI applications in a product. Collaborate with engineering to ideate and understand capabilities of LLMs.

Khan Academy offers Khanmigo as a personal tutor for every student. 1:1 tutoring is an undeniably superior experience in ed-tech.

Solution as a feature

Feature-level opportunities (eg: Linear, Notion, Grain) can take the form of inline context menus that are tightly scoped AI features.

  1. They are context specific.

  2. They allow you to use the right UI for the feature, and not retro-fit it to a conversational interface.

  3. They are lighter and make it easier to weave AI interactions into your experience.

Solution as a platform

Platform-level opportunities (eg: Microsoft Copilot, Sidekick, Bard) can take the form of a copilot/chat as a capability across your tool.

  1. Natural language just got way more powerful, as it is the medium for LLMs.

  2. Conversation makes it easy to maintain context move through a flow.

  3. It’s powerful only when you need a central hub for your use cases.

Solution as a person

Sometimes a full blown chat interaction is the solution. This is a good idea if you are emulating a real person.

Intercom, Duolingo, and Khanmigo have all identified that being conversational and personal is a core part of their products, and leaned into it.

Now you should have clarity on a high-ROI problem that only an LLM can solve, and a solution concept. It’s time to start tinkering!


is an option within its Filter feature. It makes an existing feature more powerful.

Shopify’s is an assistant that can take actions across different features of the product from a single interface.

2️⃣
Linear's AI feature
Sidekick