AI and Data
AI, analytics, data systems, and the language behind modern automation.
AI
The simulation of human intelligence processes by computer systems, enabling machines to learn, reason, solve problems, and make decisions.
Machine Learning
A subset of artificial intelligence where systems learn from data and improve their performance over time without being explicitly programmed.
Generative AI
A type of artificial intelligence capable of generating new content, such as text, images, code, or audio, based on patterns learned from training data.
LLM
A type of artificial intelligence model trained on massive amounts of text data to understand, generate, and manipulate human language.
Model
A mathematical representation of a real-world process, trained on data to recognize patterns and make predictions or decisions.
Token
A basic unit of text, such as a word or a part of a word, that a language model uses to process and generate language.
Context Window
The maximum amount of text, measured in tokens, that an AI model can read and process at a single time.
Prompt
The text, question, or set of instructions provided to an AI model to guide its output and generate a response.
Training
The process of feeding data to an AI model to help it learn patterns, adjust its internal mathematical weights, and make accurate predictions.
Inference
The process of using a trained AI model to make predictions, generate content, or solve tasks based on new, unseen input data.
Embedding
A mathematical representation of data, such as words or images, as a list of numbers that captures its meaning and relationships.
Vector Database
A specialized database designed to store, index, and rapidly search high-dimensional mathematical vectors, such as embeddings.
RAG
A technique that improves AI accuracy by searching external databases for relevant facts before generating a response.
Hallucination
A phenomenon where an AI model generates incorrect or fictional information but presents it with high confidence.
Agent
An autonomous AI system designed to perceive its environment, make decisions, use digital tools, and execute multi-step tasks to achieve a specific goal.
Fine-tuning
The process of taking an existing trained AI model and training it further on a smaller, specialized dataset to adapt it for a specific task.