AGI (Artificial General Intelligence)
A hypothetical type of AI that can understand, learn, and apply knowledge across any intellectual task a human can perform.
By Navneet Arya ·
Clear definitions of 49 AI terms — LLM, GPT, RAG, prompt engineering, fine-tuning, and more. Written for beginners, updated for 2026.
A hypothetical type of AI that can understand, learn, and apply knowledge across any intellectual task a human can perform.
An autonomous AI system that can perceive its environment, make decisions, and take actions to accomplish specific goals.
A set of rules and protocols that allows different software applications to communicate with each other.
A prompting technique that encourages a language model to break down complex reasoning into intermediate steps before arriving at a final answer.
A subset of machine learning that uses neural networks with many layers to learn complex patterns from large amounts of data.
A type of generative AI model that creates images by learning to reverse a gradual noising process. DALL·E, Midjourney, and Stable Diffusion all use this approach.
A numerical representation of text, images, or other data as a dense vector in a high-dimensional space. Embeddings capture semantic meaning.
The process of taking a pre-trained AI model and further training it on a smaller, task-specific dataset to improve its performance.
A large AI model trained on broad, diverse data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Llama, and Gemini are all foundation models.
A family of large language models developed by OpenAI that generate text by predicting the next token in a sequence.
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated.
A neural network trained on massive amounts of text data that can understand, generate, and reason about human language.
A parameter-efficient fine-tuning technique that adds small, trainable adapter layers to a frozen pre-trained model instead of updating all its weights.
AI systems that can process and generate multiple types of data — such as text, images, audio, and video — within a single model.
The field of AI focused on enabling computers to understand, interpret, and generate human language.
The practice of crafting effective instructions (prompts) to get the best possible output from an AI model.
A technique that enhances AI responses by first retrieving relevant documents from an external knowledge base, then feeding that context to the language model.
A training technique where human evaluators rank AI outputs by quality, and those rankings are used to train a reward model.
The basic unit of text that language models process — typically a word, part of a word, or punctuation mark. LLM pricing, context limits, and speed are all measured in tokens.
The neural network architecture behind virtually all modern language models, introduced in the 2017 paper "Attention Is All You Need."
A specialized database optimized for storing and searching high-dimensional vector embeddings.
A technique where an AI model performs a task it was never explicitly trained on, using only a natural language instruction.