AI Glossary

Your comprehensive guide to artificial intelligence terminology

100 essential terms from foundational concepts to emerging technologies

Foundational AI Concepts

Artificial Intelligence (AI)

Systems that simulate human intelligence processes such as learning, reasoning, and problem-solving.

Machine Learning (ML)

Algorithms that learn patterns from data without explicit programming.

Deep Learning (DL)

Subset of ML using neural networks with multiple layers to extract higher-level features.

Neural Network

Computational model inspired by the human brain, consisting of nodes (neurons) and layers.

Supervised Learning

ML approach using labeled data to train models.

Unsupervised Learning

ML approach where the system identifies patterns in unlabeled data.

Reinforcement Learning (RL)

Models learn through trial and error by receiving rewards or penalties.

Transfer Learning

Using a pre-trained model as a starting point for a new task.

Fine-tuning

Adapting a pre-trained model for a specific use case.

Foundation Model

Large pre-trained model (e.g., GPT, Claude, Gemini) that can be adapted to many downstream tasks.

Generative AI and LLM Ecosystem

LLM (Large Language Model)

AI trained on massive text datasets to generate human-like text.

Prompt Engineering

The art of crafting effective inputs to guide model outputs.

RAG (Retrieval-Augmented Generation)

Combines external data retrieval with generative models to improve accuracy.

Embeddings

Numeric vector representations of text, images, or audio used to measure similarity.

Vector Database

Specialized database for storing and searching embeddings.

Token

Smallest unit of text processed by an LLM (roughly 4 characters or 0.75 words).

Context Window

Maximum number of tokens a model can process in one prompt.

Hallucination

When a model generates false or fabricated information.

Prompt Injection

Malicious manipulation of model prompts to override its instructions.

System Prompt

Hidden instruction guiding an AI model's overall behavior or persona.

Applied AI Techniques

NLP (Natural Language Processing)

AI methods for understanding and generating human language.

Computer Vision (CV)

AI that processes and interprets visual data from images or video.

Speech-to-Text (ASR)

Converting spoken language into written text.

Text-to-Speech (TTS)

Generating human-like voice from text.

Multimodal AI

Models that process text, images, and other modalities together.

Zero-shot Learning

Making predictions on unseen classes without direct training examples.

Few-shot Learning

Learning from a very small amount of labeled examples.

Active Learning

Model queries humans for labeling the most informative samples.

Human-in-the-Loop (HITL)

Combining human oversight with automated AI systems.

Explainable AI (XAI)

Techniques to make model decisions interpretable.

Data Engineering and Pipelines

Data Pipeline

Series of steps for ingesting, cleaning, transforming, and storing data.

ETL (Extract, Transform, Load)

Classic data pipeline pattern.

ELT (Extract, Load, Transform)

Variant optimized for modern data warehouses.

Feature Store

Centralized repository for ML features.

Data Lake

Raw data storage system for unstructured data.

Data Warehouse

Structured repository optimized for analytics.

Data Governance

Policies ensuring data accuracy, security, and compliance.

Data Lineage

Tracking the flow of data through systems and transformations.

Schema

Structure defining how data is organized in a database.

Data Quality Metrics

Measurements of completeness, accuracy, and consistency.

AI Infrastructure and Platforms

MLOps

Operational framework for deploying and managing ML models.

AIOps

Applying AI to IT operations and observability.

Model Registry

Central store for managing ML models and versions.

Model Drift

When model performance degrades as data changes over time.

Inference

Running a trained model on new data to generate outputs.

Serving Layer

Infrastructure that delivers real-time predictions.

AutoML

Tools that automate model training and selection.

DataOps

Agile practices for data pipeline management.

CI/CD for ML

Continuous integration and delivery of ML code and models.

Edge AI

Running models directly on devices instead of the cloud.

Model Architectures and Math

Transformer

Neural architecture that underpins modern LLMs.

Attention Mechanism

Allows models to focus on relevant parts of input sequences.

Encoder-Decoder Architecture

Used for translation and summarization tasks.

Diffusion Model

Generative model for images and video.

GAN (Generative Adversarial Network)

Uses two neural nets competing to generate realistic outputs.

Latent Space

Abstract vector space where model representations live.

Gradient Descent

Optimization algorithm for training models.

Loss Function

Quantifies how far predictions are from the target.

Regularization

Prevents overfitting by adding constraints to model training.

Backpropagation

Algorithm for updating weights in neural networks.

Tools, APIs and Frameworks

OpenAI API

Interface to access GPT models.

Anthropic Claude

LLM known for safety and long-context reasoning.

Google Vertex AI

Google Cloud's managed AI platform.

Amazon Bedrock

AWS service offering access to multiple foundation models.

LangChain

Framework for building LLM applications with memory, tools, and data retrieval.

LlamaIndex

Framework for indexing data sources for RAG systems.

Hugging Face

Platform for hosting and sharing open-source models.

TensorFlow

Deep learning framework by Google.

PyTorch

Widely used ML framework developed by Meta.

Weights & Biases (W&B)

Tool for experiment tracking and model management.

Security, Ethics and Compliance

Data Privacy

Protection of user information from unauthorized access.

PII (Personally Identifiable Information)

Data that can identify an individual.

GDPR / DPDP

Regulations governing personal data protection.

Bias in AI

Systematic unfairness embedded in models.

Fairness Metrics

Quantitative measures to detect and mitigate bias.

Model Watermarking

Techniques to verify model ownership or detect generated content.

Adversarial Attack

Input designed to fool AI models.

Red Teaming

Stress-testing AI models for vulnerabilities or misuse.

Responsible AI

Framework ensuring AI systems are ethical, safe, and transparent.

AI Governance

Organizational oversight of AI model lifecycle and risks.

AI Applications and Use Cases

Chatbot

Conversational AI system for automated dialogue.

Agentic AI

Autonomous systems that can plan and act toward goals.

Copilot

AI assistant that augments human work.

Recommender System

Suggests items based on user preferences.

Predictive Analytics

Using historical data to forecast outcomes.

Anomaly Detection

Identifying unusual behavior or data points.

Knowledge Graph

Network of relationships among entities for reasoning.

Semantic Search

Search using meaning rather than exact keywords.

Document Intelligence

Extracting structured data from documents.

Synthetic Data

Artificially generated data used for model training.

Advanced and Emerging Topics

Agent Frameworks

Toolkits for building multi-step AI agents.

Tool Use (Function Calling)

Allowing models to interact with APIs and data sources.

Chain of Thought (CoT)

Step-by-step reasoning method in LLMs.

Tree of Thoughts (ToT)

Structured multi-path reasoning for decision-making.

Multimodal Fusion

Integrating multiple data types (text, image, audio) in one model.

LoRA (Low-Rank Adaptation)

Efficient fine-tuning technique for large models.

Quantization

Compressing models by reducing precision for faster inference.

Distillation

Training a smaller model to replicate a larger one's performance.

AutoGen / CrewAI

Frameworks for orchestrating multiple AI agents collaboratively.

Sovereign AI

Building AI models and infrastructure locally to retain data control and compliance.

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