AIFundamentals

Artificial Intelligence

Artificial Intelligence (AI) is the field of computer science concerned with building systems that perform tasks normally requiring human intelligence, such as…

Generative AI

Generative AI refers to models that create new content — text, images, audio, video, or code — rather than only classifying or predicting labels for existing d…

Machine Learning

Machine Learning (ML) is a subfield of AI in which systems improve their performance on a task by learning patterns from data, rather than following explicitly…

Deep Learning

Deep Learning is a subset of machine learning that uses artificial neural networks with many layers ('deep' architectures) to automatically learn hierarchical…

Neural Network

A neural network is a computational model composed of interconnected layers of simple processing units called neurons, loosely inspired by biological brains. E…

Prompt Engineering

Prompt engineering is the practice of designing and refining the input text (prompt) given to a language model to reliably elicit accurate, relevant, and well-…

RAG

Retrieval-Augmented Generation (RAG) is a technique that combines a language model with an external retrieval system: relevant documents or passages are fetche…

Embeddings

Embeddings are dense numerical vector representations of data — such as words, sentences, images, or documents — learned so that semantically similar items are…

LLM

A Large Language Model (LLM) is a neural network, typically based on the transformer architecture, trained on massive amounts of text to predict and generate h…

Context Window

The context window is the maximum amount of text, measured in tokens, that a language model can process and 'see' at one time, encompassing both the input prom…

Hallucination

Hallucination refers to a generative AI model producing output that is factually incorrect, fabricated, or unsupported by its training data or provided context…

Inference

Inference is the process of running a trained machine learning model on new input data to produce a prediction, classification, or generated output. It is dist…

Fine Tuning

Fine-tuning is the process of taking a pretrained machine learning model and continuing its training on a smaller, task- or domain-specific dataset to adapt it…

LoRA

LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that adapts a large pretrained model by freezing its original weights and injecting s…

RLHF

Reinforcement Learning from Human Feedback (RLHF) is a training technique used to align language models with human preferences by having people rank or rate mo…

AI Agent

An AI agent is a system, typically built around a large language model, that can perceive its environment or task, make decisions, and take autonomous actions…

Agentic AI

Agentic AI describes AI systems designed with a degree of autonomy — the ability to plan, make decisions, take actions, and adapt over multiple steps toward a…

AI Ethics

AI ethics is the interdisciplinary field concerned with identifying and addressing the moral and societal implications of designing, deploying, and using AI sy…

AI Safety

AI safety is the field focused on ensuring AI systems behave reliably, predictably, and in accordance with human intentions, avoiding unintended harmful behavi…

Vector Search

Vector search is a retrieval method that finds items by comparing the numerical similarity of their vector embeddings rather than matching exact keywords.

Semantic Search

Semantic search is an information retrieval approach that ranks results by contextual meaning and user intent rather than by exact keyword matches.

Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to a hypothetical form of AI that can understand, learn, and apply knowledge across a wide range of tasks at a lev…

Chain of Thought

Chain of thought is a prompting technique that encourages a large language model to generate intermediate reasoning steps before producing a final answer, impr…

Few-Shot Learning

Few-shot learning is a machine learning approach in which a model learns to perform a new task from only a small number of labeled examples, rather than a larg…