Few-shot / Zero-shot Learning
A model's ability to perform a new task after seeing just a few examples (few-shot) or with no examples at all, based purely on a text description of the task (zero-shot) — without any additional training on new data.
See also
Chain-of-Thought
A technique where the model "thinks out loud" step by step before giving its final answer, instead of producing a result immediately. It noticeably improves accuracy on tasks that require logic or math.
Neural Network
A mathematical model loosely inspired by the connections between neurons in the brain: many layers of simple computational units that together learn to find complex patterns in data. It's the foundation of nearly all modern AI systems.
Model Weights
The numeric parameters inside a neural network that are adjusted during training and determine how the model processes input. "Open-weight" means these parameters can be downloaded so anyone can run the model themselves.
AGI (Artificial General Intelligence)
A hypothetical AI capable of understanding, learning, and performing any intellectual task at a human level or beyond — unlike today's models, which excel in specific domains but lack general-purpose intelligence. AGI has not yet been achieved, and its timeline is a subject of debate in the industry.
Vector Database
A database optimized for storing and quickly searching embeddings (vectors) by semantic similarity rather than exact text matches. It's a key component of RAG systems and semantic search.
MCP (Model Context Protocol)
An open standard introduced by Anthropic that lets AI models connect to external data sources and tools — databases, file systems, APIs — in a uniform way, without writing a separate integration for every model-and-service combination.