AI Buzzwords

A plain-language glossary of AI terms with no jargon and no hype, just clear definitions you can actually understand.

AI Agent
An AI system that autonomously plans and executes multi-step tasks using tools and decision-making loops.
Agentic AI
AI systems designed to autonomously plan, act, and iterate toward completing complex goals.
Alignment
The field of research focused on making AI systems behave in accordance with human values and intentions.
Attention Mechanism
The neural network component that lets models focus on relevant parts of their input when generating each output.
Benchmark
A standardized test used to measure and compare AI model performance across specific tasks.
Chain of Thought
A prompting technique where the AI shows its step-by-step reasoning before giving a final answer.
Context Window
The maximum amount of text an AI model can consider at once, measured in tokens.
Diffusion Model
An AI model that generates images by learning to reverse a gradual noising process.
Distillation
Training a smaller, faster model to mimic the behavior of a larger, more capable model.
Embeddings
Numerical representations of text that capture semantic meaning, enabling AI to measure similarity between concepts.
Few-Shot Learning
An AI's ability to perform a new task after seeing just a few examples in the prompt.
Fine-tuning
Adapting a pre-trained AI model to a specific task or domain by training it on additional specialized data.
Foundation Model
A large AI model trained on broad data that serves as the base for many downstream applications.
GPT
Generative Pre-trained Transformer - OpenAI's family of language models.
Guardrails
Safety mechanisms that constrain AI behavior to prevent harmful, biased, or off-topic outputs.
Hallucination
When an AI model generates confident-sounding information that is factually incorrect or fabricated.
Inference
The process of using a trained AI model to generate outputs from new inputs.
Large Language Model (LLM)
An AI system trained on massive text datasets to understand and generate human language.
Latent Space
The compressed, abstract representation that AI models use internally to encode and manipulate concepts.
Mixture of Experts (MoE)
An architecture where only a subset of model parameters activate for each input, enabling larger models at lower compute cost.
Multimodal
AI systems that can process and generate multiple types of content - text, images, audio, and video.
Neural Network
A computing system inspired by biological brains, made of layers of interconnected nodes that learn from data.
Open Source AI
AI models whose weights and code are publicly available for anyone to use, modify, and deploy.
Parameter
A learnable value in a neural network that gets adjusted during training to improve model performance.
Prompt Engineering
The practice of crafting effective inputs to AI models to get better, more accurate outputs.
Quantization
Reducing the precision of a model's numerical values to make it smaller and faster, with minimal quality loss.
RAG (Retrieval-Augmented Generation)
A technique that gives AI models access to external knowledge by retrieving relevant documents before generating responses.
RLHF (Reinforcement Learning from Human Feedback)
A training technique that uses human preferences to align AI behavior with human values and intentions.
Synthetic Data
Training data generated by AI models rather than collected from real-world sources.
Temperature
A parameter that controls the randomness and creativity of AI model outputs.
Token
The basic unit of text that AI models process - roughly 3/4 of a word in English.
Tokenizer
The component that converts text into numerical tokens that a language model can process.
Transformer
The neural network architecture behind all modern language models, based on self-attention mechanisms.
Vector Database
A database optimized for storing and searching embedding vectors, enabling fast similarity search.
Zero-Shot
An AI's ability to perform a task it has never been explicitly shown examples of.