AI Demystified

5 Mental Models You Need Before Diving Into AI

Before you learn how AI works, learn how to think about it. These five mental models will save you hours of confusion.

What Happens When You Ask AI Something?

You type a message. Half a second later, AI replies. Here's every single step that happens in between — from your first letter to the model's last word.

Transformers — The Architecture That Changed Everything

Every major AI — GPT, Claude, Gemini, Llama — runs on the Transformer. Here's how it works, stage by stage, with live visualizations.

How AI Learns, Thinks, and Decides

Training, inference, sampling, fine-tuning — these words are everywhere. Here's what they actually mean, with live visualizations and honest analogies.

How AI Reads Your Words

AI doesn't read letters or words — it reads tokens. This small detail explains why AI costs money, why it stumbles on names, and why non-English is more expensive.

Why AI Forgets

Mid-conversation, AI suddenly doesn't remember what you said earlier. This isn't a bug — it's the context window. Here's how it works and how to work around it.

Why AI Lies (And Doesn't Know It)

AI sometimes states false things with complete confidence — fake citations, wrong numbers, invented APIs. This is hallucination. Here's why it happens and how to catch it.

What AI Cannot Do

8 structural limitations of every language model — not bugs, not temporary — built into how they work. Illustrated.

How AI Reasons (And Why It Sometimes Breaks)

o1, o3, DeepSeek-R1 — reasoning models behave differently. What chain-of-thought actually is, what 'thinking longer' means, and where it still fails.

Prompt Engineering — How to Talk to AI

Five techniques that cover 95% of real-world prompt engineering. Zero-shot, few-shot, role prompting, chain-of-thought, and structured output — with before/after examples.

Embeddings & Vector Databases — The Memory Layer of AI

How neural networks encode meaning as numbers, why similar things end up close in vector space, and what databases like Pinecone and pgvector actually do.

RAG Explained — How AI Knows What You Didn't Train It On

Retrieval-Augmented Generation lets LLMs answer questions about documents they've never seen. Here's how the pipeline works and when to use it.

Fine-tuning vs. Prompting — When to Use Which

Prompt engineering gets you 80% of the way. Fine-tuning gets you the last 20%. Here's a clear decision framework for choosing between them.

Do You Really Need GPT-4?

Frontier models are impressive — and often the wrong choice. Here's the model selection map: cost, capability, latency, and when local wins.

Latency, Tokens, and Cost — The Physics of AI Products

Why is AI slow? Why does it cost money? What does streaming actually change? The mechanics of inference, visualized.

How Do You Know AI Is Actually Working?

Demos always look good. Production AI degrades silently. Here's the evaluation framework — from exact match to human review — and how to catch hallucinations.