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Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI applications connect to external data sources and tools. MCP provides a universal interface for LLMs to access databases, APIs, file systems, and other services through standardized server implementations. It enables building AI applications that can interact with the real world in a structured, secure way.

In AI, hallucination refers to when a language model generates confident-sounding but factually incorrect or fabricated information. This occurs because LLMs predict statistically likely text rather than retrieving verified facts. Mitigation strategies include RAG, grounding responses in source documents, structured output validation, and using temperature settings to reduce creative deviation.

A neural network is a computational model inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process data by adjusting weighted connections during training. Deep neural networks with many layers form the foundation of modern AI, powering everything from image recognition to language understanding. Common architectures include feedforward networks, convolutional networks (CNNs), and transformers.

Generative AI is a category of artificial intelligence that creates new content — text, images, code, music, or video — rather than just analyzing or classifying existing data. Powered by architectures like transformers and diffusion models, generative AI has transformed software development with tools like GitHub Copilot, Claude, and Cursor. It represents a shift from AI as a classification tool to AI as a creative collaborator.

Reinforcement learning (RL) is a machine learning paradigm where an agent learns optimal behavior by interacting with an environment and receiving rewards or penalties. RLHF (Reinforcement Learning from Human Feedback) is a key technique used to align LLMs with human preferences, making their outputs more helpful and safe. RL is also behind breakthroughs in game-playing AI and robotics.

Multimodal AI refers to models that can process and generate multiple types of data — such as text, images, audio, and video — within a single system. Models like GPT-4o and Claude can accept both text and image inputs, enabling use cases like visual question answering, document analysis, and UI understanding. This convergence is blurring the lines between previously separate AI disciplines.