Generative AI Explained

AI with human-level intelligence across all intellectual tasks, capable of understanding, learning, and applying intelligence broadly.

Autonomous AI systems that perceive their environment, process information, make decisions, and take actions to achieve specific goals, often interacting with humans or other systems.

The broad field of computer science dedicated to creating machines that can perform tasks typically requiring human intelligence.

AI far surpassing human intelligence in all fields, including creativity, wisdom, and social skills.

A prompting technique that asks a model to generate its intermediate reasoning steps before giving a final answer.

The amount of text (measured in Tokens) that an Large Language Model (LLM) can consider at one time when processing an input or generating a response. It defines the AI's "short-term memory" for a given interaction.

A powerful Generative AI model that creates new data (especially images and more recently with text) by gradually reversing a noise-adding process, "denoising" from pure static to a clear image.

Numerical representations capturing the meaning or characteristics of data (words, sentences, images); similar meanings have similar numerical representations.

Further training an already developed Generative AI model on a smaller, specific dataset to adapt it for a particular task or domain.

A tool for exploring the internal neurons of a GAN, showing how specific units correspond to interpretable concepts like "tree," "door," or "tower."

An interactive in-browser tool that helps users visualize and train simple GANs (Generator & Discriminator) step-by-step on toy 2D distributions.

A Generative AI model with two competing neural networks: a "Generator" creating fakes, and a "Discriminator" identifying them, improving through competition.

AI that creates content by predicting the next token (text, images, music, code), rather than just analyzing existing data.

When a Generative AI model confidently produces false, nonsensical, or unsubstantiated information. It "makes things up."

The process where a trained AI model takes a new input (like a Prompt) and generates an output or makes a prediction. It's the "runtime" phase of an AI model.

The process of fine-tuning a pre-trained model on paired examples of instructions and ideal responses to make it better at following user commands.

A powerful AI trained on vast text data to understand, generate, and respond to human language naturally.

A high-dimensional space where complex data (like images, text, or audio) is encoded as numerical vectors capturing its essential features.

A parameter-efficient fine-tuning method that injects small, trainable low-rank matrices into a frozen pre-trained model.

A subset of Artificial Intelligence (AI) that focuses on enabling systems to learn from data, identify patterns, and make decisions with minimal human intervention.

A standardized set of rules or an interface that allows an AI model (especially LLMs and AI agents) to effectively connect with and use external tools, functions, or APIs to accomplish tasks.

Internal values or "knobs" within an AI model adjusted during Training Process; more parameters allow learning more complex patterns.

AI systems capable of processing and generating content across multiple types of data simultaneously, such as text, images, audio, and video.

A computational model inspired by the structure and function of the human brain, consisting of interconnected "nodes" or "neurons" organized in layers, designed to recognize patterns and learn from data.

The instruction or input given to a Generative AI model to guide its output.

The skill of crafting effective Prompts to achieve desired AI results by understanding how the AI "thinks".

AI models designed to "think" step-by-step, performing logical inference, planning, and problem-solving, rather than simply predicting the next token based on probability.

Humans rate Generative AI outputs, and this feedback further trains the AI to produce more helpful, accurate, and aligned responses.

A technique that enhances a Large Language Model (LLM) by allowing it to retrieve relevant information from an external, trusted knowledge base (like a database or documents) before generating a response.

A network component that lets a model weigh the importance of each input element relative to others when producing an output.

Hyperparameters controlling the randomness of a model's next-token selection—temperature adjusts distribution "sharpness," top-p limits sampling to the most probable tokens.

The fundamental "building block" of text processed and generated by a Large Language Model (LLM), typically part of a word, a whole word, or punctuation.

The vast information (text, images, code) an AI model learns from during initial development, forming its knowledge and abilities.

A neural network design enabling AI to process and generate sequences (like text) by efficiently "paying attention" to relevant parts of the input, regardless of distance.

An emerging approach that explores multiple parallel chains of thought (branches), evaluates them, and backtracks to the most promising paths.

A specialized database optimized for storing and retrieving high-dimensional vectors based on similarity search.

Techniques where a model performs a task with zero, one, or only a handful of examples provided at inference time.