An interactive way to understand the core concepts behind artificial intelligence.
The smallest unit of text a model processes. It can be a word, part of a word, or even a symbol.
The process of breaking text into small units (tokens) a model can understand. Each token can be a word, subword, or character.
The model turns each token into numbers that represent its meaning. Tokens with similar meanings have vectors (points positioned in space) that are close together.
The limit of how much text a model can consider at once. It reads and reasons only within this window, measured in tokens.
An internal map where the model organizes what it has learned. Each point represents a concept, and similar ideas group close together.
A network of connected layers that learn from examples. Each layer refines the data, and together they learn patterns used to recognize images, understand language, or process sounds.
Values the model learns during training that determine how strongly different parts of the network connect and respond. Together, they define how the model understands and generates information.
A system that has learned from data and can now use that knowledge to predict, generate, or understand new information.
A type of neural network that looks at every word in a sequence at once. Unlike earlier models that read step by step, it learns how words relate across the whole text, allowing it to understand context much more effectively.
A mechanism inside Transformers that decides which words to focus on when processing a sentence. Each word looks at others and assigns more weight to the ones that matter most for understanding.
The first learning stage where a model trains on vast text data to learn patterns, context, and general knowledge.
Training a pre-trained model on new, specific data so it adapts to a particular task or tone. It keeps what it already knows but learns to apply it in a focused way.
A training method where the model improves through feedback. It tries actions, receives rewards or penalties from humans or another model, and learns to make better decisions over time.
Step-by-step reasoning the model writes to reach an answer. It helps the model break complex problems into smaller, more manageable steps.
The stage where a trained model uses what it has learned to generate a response. It predicts the next token step by step until the answer is complete.
A method that lets a model look up information before answering. It retrieves relevant data from external sources, then uses that context to write a more complete answer.
Agents are autonomous systems that use tools and feedback loops to accomplish tasks.
A predefined sequence of steps where each stage uses the previous result to move the task forward toward a final outcome.
A very large neural network trained on vast text data to understand, predict, and generate human language.
I built this guide to clarify the concepts behind the tools we use every day.
Thanks to Fflur Page for the help. If you build AI products and need help with the interface, feel free to reach out. You can also explore prompt-kit, the core building blocks for AI apps.