AI Encyclopedia

Agentic AI

Agentic AI refers to AI systems built around autonomy, the ability to plan, decide, and take a series of actions toward a goal, rather than just answering one question and stopping. This entry explains what makes AI "agentic," how it differs from a single AI agent and from generative AI, using simple analogies anyone can follow.

AGI (Artificial General Intelligence)

AGI, short for Artificial General Intelligence, refers to a hypothetical AI system that could understand, learn, and apply intelligence across virtually any task at a human level, rather than being limited to a specific domain. This entry explains what the term actually means, why it remains genuinely debated, using simple analogies anyone can follow.

AI (Artificial Intelligence)

Artificial intelligence is the broad field of building machines and software that can perform tasks normally associated with human thinking, such as recognizing patterns, understanding language, and making decisions. This entry breaks down what AI actually means, using simple analogies and everyday examples, so the idea makes sense even if you have never written a line of code.

AI Agents

An AI agent is a software system that can perceive its environment

AI Automation

AI automation is the use of artificial intelligence to handle tasks and workflows that would otherwise require ongoing human effort, capable of making real decisions and adapting to changing situations rather than just following a fixed set of steps. This entry explains how AI automation actually works, using simple analogies anyone can follow.

AI Bias

AI bias refers to systematic, unfair patterns in an AI system's outputs that disadvantage particular groups, typically arising because the underlying training data reflected existing imbalances rather than the system making a deliberate choice. This entry explains how AI bias actually happens, using simple analogies anyone can follow.

API

An API, short for Application Programming Interface, is the defined way one piece of software can request data or actions from another, without needing to know how that other system works internally. This entry explains what an API really is, using simple analogies, and how it connects directly to AI tools and automation.

Computer Vision

Computer vision is the branch of AI focused on enabling computers to interpret and understand visual information, such as photos and video, turning a meaningless grid of pixels into genuine recognition of what is actually depicted. This entry explains how computer vision actually works, using simple analogies anyone can follow.

Context Engineering

Context engineering is the practice of deliberately designing and managing everything fed into an AI model's context window, beyond just the immediate instruction, so the model has exactly the right information available without being overloaded. This entry explains how context engineering differs from prompt engineering, using simple analogies anyone can follow.

Context Window

A context window is the maximum amount of text an AI model can consider at once, measured in tokens, covering everything from your instructions to the conversation history to its own response. This entry explains what a context window really means, using simple analogies anyone can follow.

Data Analysis

Data analysis is the process of examining raw numbers and records to find meaningful patterns and support better decisions, turning information that means little on its own into a clear, useful story. This entry explains how data analysis actually works, using simple analogies anyone can follow.

Deep Learning

Deep learning is a specific approach within machine learning that uses many-layered neural networks to automatically discover useful patterns directly from raw data, without a person needing to manually decide what to look for. This entry explains what makes it "deep" and why that matters, using simple analogies anyone can follow.

Deepfake

A deepfake is synthetic media, typically video, image, or audio, created using AI to make it appear that a real person said or did something they never actually said or did. This entry explains how deepfakes work, the real harms and legitimate uses involved, and why detecting them remains genuinely difficult.

Embedding & Vector Database

An embedding turns a piece of text into a list of numbers that captures its meaning, while a vector database stores millions of these embeddings so similar pieces of content can be found instantly. This entry explains how both actually work, using simple analogies anyone can follow.

Fine-Tuning

Fine-tuning is the process of taking an already-trained AI model and continuing its training on a smaller, more focused set of examples, so it becomes specialized for a particular task, tone, or domain. This entry explains how fine-tuning actually works, and how it compares to prompt engineering and RAG, using simple analogies anyone can follow.

Generative AI

Generative AI is a category of AI that creates new content, such as text, images, audio, video, or code, rather than simply analyzing or sorting existing data. This entry explains what makes AI "generative" and how it differs from prediction-focused AI, using simple analogies anyone can follow.

GPT (Generative Pre-trained Transformer)

GPT stands for Generative Pre-trained Transformer, both a specific AI architecture and the name OpenAI uses for its family of large language models, the technology behind ChatGPT. This entry explains what the name actually means and how GPT fits into everything else covered in this series, using simple analogies anyone can follow.

Hallucination

Hallucination is when an AI model generates information that sounds confident and well written but is actually false or made up. This entry explains why it happens, using simple analogies and real examples, and what can be done to reduce the risk.

Knowledge Cutoff

A knowledge cutoff is the point in time up to which an AI model was trained, meaning it has no built-in awareness of anything that happened after that date. This entry explains what a knowledge cutoff really means, using simple analogies anyone can follow.

LLM (Large Language Model)

A large language model, or LLM, is the technology behind tools like ChatGPT and Claude. It is a type of AI trained on massive amounts of text that learns to predict and generate human-like language. This entry explains what an LLM actually is, using simple analogies and everyday examples.

Machine Learning

Machine learning is the branch of AI focused on building systems that learn patterns from data and improve over time, rather than being explicitly programmed with a fixed rule for every situation. This entry explains how machine learning actually works, using simple analogies anyone can follow.

MCP (Model Context Protocol)

MCP, short for Model Context Protocol, is an open standard that lets AI models and assistants connect to external tools and data sources in one common way, instead of needing a custom-built connection for every single pairing. This entry explains what MCP really is, using simple analogies anyone can follow.

Multimodal AI

Multimodal AI refers to AI systems that can understand, process, and sometimes generate more than one type of information at once, such as text, images, audio, and video, combined together rather than handled separately. This entry explains how multimodal AI actually works, using simple analogies anyone can follow.

Neural Network

A neural network is a type of AI system loosely modeled on how neurons in the brain connect and pass signals, built from simple layered units that work together to recognize patterns in data. This entry explains how a neural network actually works, using simple analogies anyone can follow.

NLP (Natural Language Processing)

NLP, short for Natural Language Processing, is the branch of AI focused on enabling computers to understand, interpret, and generate human language, in both written and spoken form. This entry explains what NLP actually covers, and how today's large language models fit into this much older field, using simple analogies anyone can follow.

Prompt Engineering

Prompt engineering is the practice of carefully crafting instructions to get the best possible output from an AI model, without changing the model itself. This entry covers the core idea along with key techniques like few-shot prompting, chain of thought, role prompting, system prompts, prompt chaining, and structured output, using simple analogies anyone can follow.

RAG (Retrieval Augmented Generation)

RAG, short for Retrieval Augmented Generation, is a technique that lets an AI model look up relevant information from an external source before answering, instead of relying purely on what it memorized during training. This entry explains how RAG actually works, using simple analogies anyone can follow.

RLHF

RLHF, short for Reinforcement Learning from Human Feedback, is the training technique that shapes a raw, pretrained language model into a genuinely helpful, well behaved assistant by teaching it from human preferences rather than just predicting plausible text. This entry explains how RLHF actually works, using simple analogies anyone can follow.

Temperature

Temperature is a setting that controls how predictable or random an AI model's output is, from safe and consistent answers to more varied and creative ones. This entry explains what temperature actually does, using simple analogies anyone can follow.

Token

A token is the basic unit of text that an AI language model actually reads and writes, smaller than a full sentence and often smaller than a full word. This entry explains what a token really is, why it matters for cost and performance, using simple analogies anyone can follow.

Transformer Architecture

The transformer architecture is the specific neural network design behind nearly every modern large language model, built around a mechanism called attention that lets a model weigh the importance of every word in relation to every other word at once. This entry explains how transformers actually work, using simple analogies anyone can follow.