fundamentals
Temperature and top-p: how an AI actually picks its next word Lesson
A language model does not know its next word - it produces a list of odds and then rolls dice. The rules of that dice roll are why the same prompt gives you a boring answer one day and a wild one the next.
Backpropagation: how a neural network learns from its mistakes Lesson
The single algorithm behind nearly all AI training - assigning blame for an error backward through millions of dials, so each one knows which way to turn.
Retrieval-Augmented Generation: giving a model an open book Lesson
A language model only knows what it learned in training, and it cannot cite sources. RAG fixes both by letting the model look things up in a real document store at answer time, then write its answer from what it found.
Embeddings: how AI turns words into directions in space Lesson
Before a model can reason about a word, an image, or a paragraph, it has to turn it into a list of numbers -- a vector. Embeddings are how meaning gets encoded as position in space, so that closeness becomes similarity.
Speculative Decoding: How AI Types Faster Without Changing a Word Lesson
A small, fast model guesses the next few words and a big, slow model checks them all in one pass - producing the exact same output, just quicker. The trick behind a lot of modern AI speedups.
Quantization: Shrinking AI Models to Run on Modest Hardware Lesson
Storing a model's numbers with less precision - 8, 4, or even fewer bits instead of 16 - makes it dramatically smaller and faster, often with almost no loss in quality. It's why big models can run on a laptop or a single GPU.
Transformers: the engine inside almost every modern AI Lesson
The neural-network design behind GPT, Claude, and nearly every modern AI model, and the one idea, attention, that made it work.
Tokenization: how an AI chops your words into pieces it can read Lesson
A language model never sees letters or words. It sees tokens, chunks of text turned into numbers. How that chopping works quietly shapes cost, context limits, multilingual fairness, and why models stumble on spelling and arithmetic.
Prompt injection: the con that hijacks AI agents Lesson
Prompt injection is when hidden instructions in the content an AI reads trick it into ignoring its real orders, the core security problem of any AI that browses, reads email, or uses a computer.
Distillation: how a small AI learns from a big one Lesson
Distillation trains a smaller, cheaper model to imitate a larger, smarter one, the idea behind both efficient deployment and the 'copying' accusations now driving AI geopolitics.
How AI Gets Benchmarked — and Why the Leaderboard Can Lie Lesson
Every 'this AI is now #1' headline rests on a benchmark. Here's how those tests actually work, why a top score doesn't always mean what you think, and how to read a leaderboard like a skeptic.