AI & Machine Learning
Models, training, and putting AI into real products - without the hype or the hand-waving.
Watch it animated → Interactive, click-through walkthroughs for AI & Machine Learning concepts.Basic
What AI & Machine Learning Actually Are AI is a big umbrella, machine learning is the part of it that learns rules from examples instead of being hand-coded, and today's chatbots are powerful pattern-matchers - not minds - that can be confidently wrong.
How a Model Learns (Training, in Plain English) What 'training' actually does to a machine learning model - a model is a bundle of adjustable numbers, and training nudges those numbers until its predictions match known examples.
Using an LLM API in Your App Calling a hosted language model is a normal HTTP request: you POST a list of messages, you get back generated text - and this guide builds the mental model, the cost picture, and the reliability habits you need to ship it without a foot-gun.
Prompt Engineering, Honestly What actually improves the output of a language model - clear, specific, well-structured instructions - and what's just folklore, explained without the hype.
Prompt Engineering, Plainly What actually improves the output of a language model - clear, specific, well-structured instructions - and what's just folklore, explained without the hype.
Intermediate
Embeddings & Vector Search, Explained What an embedding actually is (meaning turned into a list of numbers), how 'nearness' between two pieces of text is measured, and how vector databases search millions of them by meaning instead of keywords.
RAG (Retrieval-Augmented Generation), Explained What RAG actually is - retrieving the right facts from your own data first, then asking the model to answer using them - why an LLM needs it, how the pipeline works end to end, and why good RAG is mostly good retrieval.
Running Models Locally What it really means to run an LLM on your own machine - the clear-eyed trade-off against a hosted API, a real Ollama session from download to local API call, and how model size, RAM/VRAM, and quantization decide whether it runs at all.
Evaluating LLM Output Evals, not vibes: how to measure whether an LLM feature actually works, catch prompt regressions, and ship changes with confidence.
Building an AI Agent What an agent actually is under the hood: the model plus tools plus a loop. Function-calling, the reasoning-acting cycle, and where agents go wrong.
Prompt Injection and Guardrails Why untrusted text in an LLM's prompt is dangerous, how injection hijacks the model, and the guardrails that actually contain it.
How a Neural Network Is Structured The structural anatomy of a neural network - layers, weights, biases, activation functions, and how one prediction flows through it - without touching how training works.
Advanced
Fine-Tuning vs Prompting, Plainly When training your own model is - and isn't - worth it: prompting steers at request time, RAG adds knowledge, fine-tuning changes the model's default behavior, and the no-nonsense order is to try them cheapest-first.