Series
AI Fundamentals
A guided learning path: neurons, training, tokens, embeddings, transformers, RAG, and responsible AI — in the order that makes them click.
14 parts · 165 min total
Start with Part 1Part 1: Part 4 — The Artificial Neuron: The Tiny Decision Machine Behind Every AI
Your brain has 86 billion neurons. ChatGPT has trillions. Today we isolate just one digital brain cell and watch it make a decision — in Python.
Part 2: Part 5 — How AI Actually Learns: The Training Loop Explained
AI isn't programmed — it's trained. Four steps, repeated millions of times: guess, measure the mistake, find who's responsible, fix it. Here's exactly how that works.
Part 3: Part 6 — From Zero to ChatGPT: The 4 Learning Types That Built Modern AI
ChatGPT didn't just learn — it learned in four completely different ways. Discover how Supervised, Unsupervised, Reinforcement, and Self-Supervised learning combine in a secret 3-step pipeline to turn raw text into a helpful, safe, and eloquent AI.
Part 4: Part 1 — Tokens: Why Your Language Costs More Than English When You Use AI
The AI doesn't read your words — it reads numbers. And those numbers aren't equal across languages. Here's what tokens are, why they cost money, and why non-English speakers pay more.
Part 5: Part 8 — Token-by-Token: Why AI Generates Text One Word at a Time (And Why It Costs 4x More)
ChatGPT doesn't think its answer in advance — it predicts one token at a time in a recursive loop. Understanding this changes how you design prompts, control costs, and build responsive AI applications.
Part 6: Part 2 — Embeddings: How AI Understands the World Without Knowing a Single Word
If AI doesn't read words, how does it know that 'Apple' the fruit is different from 'Apple' the company? The secret is in a 10,000-dimensional galaxy of numbers called Embeddings.
Part 7: Part 3 — How AI Finds Your Answer in 30 Million Documents in Under a Second
The old internet searched for words. The new internet searches for meaning. Here's the math that killed keyword search — and powers everything from TikTok to RAG.
Part 8: Part 7 — The Transformer: The Architecture That Accidentally Changed the World
In 2017, Google researchers published a paper called 'Attention Is All You Need' — and didn't patent it. That decision launched ChatGPT, Claude, Gemini, and the entire modern AI industry. Here is the complete story of the architecture that changed everything.
Part 9: Part 9 — Context Window: The Invisible Limit That Shapes Every AI Conversation
Every LLM has a context window — a hard limit on how much it can 'see' at once. Understanding what fills it up, why models forget things, and the 'Lost in the Middle' effect will change how you architect every AI feature you build.
Part 10: Part 14 — RAG: Give Your AI a Memory
ChatGPT confidently recommended a smart glasses model discontinued two years ago. The fix doesn't require retraining anything. RAG — Retrieval Augmented Generation — lets any LLM answer from YOUR data, in real time, without a single GPU.
Part 11: Part 12 — RLHF: How Humans Taught AI to Be Helpful
GPT-3 was brilliant but chaotic — it would answer 'How do I bake a cake?' with a story about a Belgian chocolate factory in 1923. RLHF is the secret process that transformed raw AI intelligence into the helpful assistant you use every day.
Part 12: Part 13 — Prompt Engineering: The Art of Talking to AI
The model isn't getting dumber. Your prompts are getting lazier. Learn the 5 techniques that separate vague, frustrating AI interactions from precise, powerful ones — including the exact RAG prompt template used in production systems.
Part 13: Part 10 — Hallucination: Why AI Lies With Complete Confidence (And How to Stop It)
An AI chatbot invented a refund policy that cost a company $650. Lawyers filed AI-generated case citations that didn't exist. AI confidently fabricates because it can't say 'I don't know' — here's the root cause, the four danger tiers, and the five solutions that actually work.
Part 14: Responsible AI: From Google's 7 Principles to Bias Detection Code and CI/CD Fairness Gates
Build responsible AI using Google's proven framework: 7 core principles, 4 hard limits, the issue spotting process, Python bias detection with demographic parity and equalized odds, and GitHub Actions CI/CD gates that block biased models from shipping.