🏫
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 Understand the tech
  • 2️⃣ Further reading
  1. Working with Features
  2. Building with AI

Understand the tech

PreviousBuilding with AI NextMap your product

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

Large Language Models provide a way for computers to understand natural human language and respond meaningfully. This was not possible prior to 2022.

1️⃣ Understand the tech

Think of LLMs as small reasoning engines rather than as a magical black box to throw stuff into. The small reasoning engine approach makes it reliable, reduce hallucinations and reduce cost of operation as well.

Try not to misuse the LLM - like don’t answer someone’s birthday using an LLM just because you can. Using an LLM for it is less reliable and more expensive than querying a database.

2️⃣ Further reading

1️⃣
LogoPrompt Engineering Guide – Nextra
LogoOpenAI API
LogoSquish Meets Structure: Designing with Language ModelsMaggie Appleton
LogoWhat is retrieval-augmented generation? | IBM Research BlogIBM