A Pragmatic
Introduction to AI

Realistic workflows, common traps, and actual value for product teams.

4 key parts

  1. Intellectual Setup
    • Glossary + Cold truths about AI.
  2. Communication & Workflow
    • A framework for teams.
  3. Demos
    • How to use the tools you already have.
  4. Mental models & Principles
    • Why this matters and how I think about AI.

Essential concepts

1. Intellectual Setup

LLM (Large Language Model)

  • Autocomplete on steroids - predicts next words based on patterns.
  • Trained on billions of organic + synthetic datasets, but doesn't "understand" - just matches patterns (more on this later).
  • Why it matters:  Explains why are great at writing/coding (patterns) → Bad at math/recent events (needs real knowledge).

Context (Your AI's Memory)

  • Everything the AI can "see" in your current conversation.
  • Limited window - like RAM, not hard drive (typically 4k-200k words).
  • Why it matters: Paste relevant info upfront. AI can't remember what it can't see.

Hallucination (Confident Bullshit)

  • When AI makes things up but sounds 100% certain.
  • Happens more with: recent events, specific numbers, obscure topics, URLs.
  • Why it matters: Always verify facts, dates, numbers, and claims.

Drift (Losing the Plot)

  • AI gradually forgetting instructions or changing style over long conversations.
  • Like a game of telephone - quality degrades over time.
  • Why it matters: For long sessions, periodically remind AI of the task (compacting context).

Token (The Currency)

  • How AI "sees" text - roughly 1 token = 4 characters.
  • You pay per token (input + output).
  • Why it matters: Verbose prompts = expensive. Be clear but concise.

A dose of reality

1. Intellectual Setup

AI Doesn't Understand Anything

  • Next time you get a response from AI, remember this: It has no idea what it's saying.

  • It doesn't "know" what a doctor is.
  • It doesn't "understand" your problems.
  • It doesn't "comprehend" who or what you are.
  • It's just INSANELY good at predicting what word should come next.
  • Why it matters: Start giving better patterns to match. That's what prompting really is.

AI Can't Create Anything "New"

  • Every single output is a recombination of patterns from training data.
  • AI has never had an original "thought", and never will.
  • AI can create combinations that you have never seen before.
  • Why it matters: AI won't invent new physics laws, but it WILL solve YOUR specific problem in ways you hadn't considered. And that's enough.

AI inherited our biases because it learned from us. If you communicate poorly with humans, you will communicate poorly with AI (the contrary is also true). The principles are the same: Context, Clarity, and Patience."

Core Idea 🔥

A practical guide

2. Communication & Workflow

The missing link

  • Cooperation = Your part, my part, fingers crossed.
     
  • Collaboration = Our solution, shared understanding.
     
  • The difference? AI as enabler.

From Cooperation to Collaboration

  • AI can be the universal translator we've needed.

  • PMs can create working prototypes → Engineers see the vision, not just requirements.
  • Engineers can generate UI mockups → Designers better understand technical constraints.
  • Designers can approach technical documentation → Developers get more accurate implementations.
  • Everyone can validate* assumptions → No more "I thought you meant...".

The missing link

From Cooperation to Collaboration

Explore

Plan

Execute

Preserve

Clean

Gather

Contextualize

Document

Operate

Reset

My AI Workflow

But this isn't just an engineering workflow... it's how all knowledge work happens!.

This isn't just an engineering workflow - it's how all knowledge work happens:

Explore

Gather context, understand the domain area.

AI helps: Research, competitive analysis, brainstorming ideation.

How AI Fits Into Any Process

How AI Fits Into Any Process

This isn't just an engineering workflow - it's how all knowledge work happens:

How AI Fits Into Any Process

Plan

Contextualize and structure your approach before execution.

AI helps: AI helps: Break down complexity, reduce cognitive load, create roadmaps, identify risks.

How AI Fits Into Any Process

This isn't just an engineering workflow - it's how all knowledge work happens:

How AI Fits Into Any Process

Execute

Operate and build (create) the actual solution.

AI helps: Generate first drafts, prototypes, code, designs... artifacts.

How AI Fits Into Any Process

This isn't just an engineering workflow - it's how all knowledge work happens:

How AI Fits Into Any Process

Preserve

Document decisions and learnings.

AI helps: Create documentation, meeting notes, PR's, decision records.

How AI Fits Into Any Process

This isn't just an engineering workflow - it's how all knowledge work happens:

Clean

Reset and refine for the next iteration.

AI helps: Observe, reduce, refactor, simplify, extract patterns.

AI is the translation layer our team needs.
Not to replace individual expertise, but to share it.
Everyone keeps their craft. Everyone speaks product.
But only if also everyone's willing to learn.

Core Idea 🔥

AI in action

3. Demos

How to Create a Good Prompt

Context + Role + Task + Format = Who + What + Why + How

Help me with a PRD for an appointment scheduler system.

I'm a PM at a healthtech company. I need a PRD for appointment scheduling v3. Users are doctors managing 50+ daily appointments. Success means 30% less time scheduling. Give me structure with user stories, success metrics, and technical requirements."

The Power of LLM's

  • Instant Feedback.
    • No more waiting for code review, design critique, or copy approval. 
  • Multimodal.
    • Upload screenshots, diagrams, photos or audio. Get analysis, code, or improvements back. 
  • Infinite Patience.
    • Ask the same question 50 different ways. Explain it like you're 5, then like you're a PhD.

Real Situations We All Face

Instant Data Analysis

  • Paste CSV data or metrics.
  • Prompt: "What patterns do you see? What should I investigate?".
  • No Excel gymnastics needed.

Real Situations We All Face

Slack: from mess to clarity

  • Problem: 73-message thread about a feature decision.
  • Prompt: "Extract decisions, action items, and owners from this conversation: [paste]"
  • Result: Chaos to clarity in 15 seconds.
  • Works for: Meeting notes, email threads, feedback sessions.

Real Situations We All Face

Esoteric Error Messages → Basic Understanding

  • Problem: "Cannot read properties of undefined (reading '0') at Array.reduce (<anonymous>) at Object.fromEntries (<anonymous>) at AsyncGeneratorFunction.next (<anonymous>".
  • Prompt: "Explain this error in plain English and suggest fixes: [paste error]".
  • Result: Understanding about what broke and why.
  • Bonus: Add your code context for specific solutions.

Real Situations We All Face

UI Variations → Instant Options

  • Problem: Need different versions for A/B testing.
  • Prompt: "Give me 10 variations of this CTA button. text: 'Get Started'. Context: medical booking app".
  • Result: From "Book Now" to "Find Your Doctor" - instant testing material.
  • Works for: Email subjects, headlines, microcopy, error messages.

Real Situations We All Face

 Technical → Business Terms

  • Scenario: Dev needs buy-in for refactoring.
  • Prompt: "Explain this technical work in business impact: We need to migrate from REST to GraphQL".
  • Output: "This change will reduce page load time by 40%, improving user retention and reducing server costs by $10k/month".

Real Situations We All Face

Business → Technical Terms

  • Scenario: PM has vague requirement.
  • Prompt: "Convert this to technical acceptance criteria: Users should be able to easily find their past appointments".
  • Output: Specific implementation requirements with edge cases.

Real Situations We All Face

Anything → Non-Technical People

  • Scenario: Need to explain complex work to stakeholders.
  • Prompt: "Explain this for a smart person with no technical background: [paste]".
  • Output: Clear explanation without patronizing.

Augmentation before transformation.
First: Fix your daily pain points
Then: Optimize your workflows
Later: Reinvent how you work.

Core Idea 🔥

My own philosophy

4. Mental models & Principles

Discomfort as Signal

  • Resistance to AI is human and shows you care about your craft.
  • Your rejection doesn't slow down change; it only slows YOU down.
  • Key insight: The friction isn't generated by technology - you generate it.

Massive Opportunity

  • Biggest "reset" in knowledge work we've ever seen.
  • Barriers (titles, years of experience) have lost some of their power.
  • Real redistribution of opportunities - a level playing field for those willing to relearn.
  • Yesterday's advantage → Today's irrelevance.
  • Today's threat → Tomorrow's career catalyst.

Radical Commitment

  • No nostalgia, no turning back. This is not a trend nor a fad.
  • When the AI wave settles: nothing will be the same (routines, tools, standards, market value).
  • Half-hearted acceptance isn't enough.
  • Only option that matters: Go all in.

What to be aware of

  • Beware of FOMO.
    • AI is just a tool, not magic.
  • Ignore the Gurus.
    • Nobody, N-O-B-O-D-Y knows where AI is going.
  • Understand what "Vibe Coding" is.
    • Professional development is still hard - AI doesn't change that.

What to look for

  • Educate Yourself.
    • Invest in AI tools YOU WILL USE (NO FOMO as we saw).
  • Don't Self-Replace.
    • Create artificial friction - too embedded assistance makes you a spectator.
  • Scratch Your Own Itch.
    • Start from a real problem you really care about

Every industry transformation creates resistance, as nobody likes change. With AI, that resistance is internal. Push through it. This can be one of the biggest challenges of your career. But also one of the biggest opportunities.

Core Idea 🔥

Thank you 🙏

A Pragmatic Introduction to AI

By Juan Andrés Núñez

A Pragmatic Introduction to AI

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