AI Glossary

Your comprehensive guide to artificial intelligence terminology

100 essential terms from foundational concepts to emerging technologies

Generative AI and LLM Ecosystem

RAG (Retrieval-Augmented Generation)

Combines external data retrieval with generative models to improve accuracy.

Prompt Engineering

The art of crafting effective inputs to guide model outputs.

Token

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

System Prompt

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

LLM (Large Language Model)

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

Context Window

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

Hallucination

When a model generates false or fabricated information.

Vector Database

Specialized database for storing and searching embeddings.

Embeddings

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

Prompt Injection

Malicious manipulation of model prompts to override its instructions.

Data Engineering and Pipelines

Feature Store

Centralized repository for ML features.

ELT (Extract, Load, Transform)

Variant optimized for modern data warehouses.

Schema

Structure defining how data is organized in a database.

Data Governance

Policies ensuring data accuracy, security, and compliance.

Data Warehouse

Structured repository optimized for analytics.

Data Lineage

Tracking the flow of data through systems and transformations.

ETL (Extract, Transform, Load)

Classic data pipeline pattern.

Data Quality Metrics

Measurements of completeness, accuracy, and consistency.

Data Lake

Raw data storage system for unstructured data.

Data Pipeline

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

Model Architectures and Math

Regularization

Prevents overfitting by adding constraints to model training.

Transformer

Neural architecture that underpins modern LLMs.

Encoder-Decoder Architecture

Used for translation and summarization tasks.

Diffusion Model

Generative model for images and video.

Gradient Descent

Optimization algorithm for training models.

GAN (Generative Adversarial Network)

Uses two neural nets competing to generate realistic outputs.

Loss Function

Quantifies how far predictions are from the target.

Attention Mechanism

Allows models to focus on relevant parts of input sequences.

Backpropagation

Algorithm for updating weights in neural networks.

Latent Space

Abstract vector space where model representations live.

Applied AI Techniques

Human-in-the-Loop (HITL)

Combining human oversight with automated AI systems.

Computer Vision (CV)

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

NLP (Natural Language Processing)

AI methods for understanding and generating human language.

Active Learning

Model queries humans for labeling the most informative samples.

Multimodal AI

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

Speech-to-Text (ASR)

Converting spoken language into written text.

Few-shot Learning

Learning from a very small amount of labeled examples.

Explainable AI (XAI)

Techniques to make model decisions interpretable.

Text-to-Speech (TTS)

Generating human-like voice from text.

Zero-shot Learning

Making predictions on unseen classes without direct training examples.

AI Infrastructure and Platforms

AutoML

Tools that automate model training and selection.

Model Registry

Central store for managing ML models and versions.

Edge AI

Running models directly on devices instead of the cloud.

Serving Layer

Infrastructure that delivers real-time predictions.

AIOps

Applying AI to IT operations and observability.

DataOps

Agile practices for data pipeline management.

CI/CD for ML

Continuous integration and delivery of ML code and models.

Inference

Running a trained model on new data to generate outputs.

MLOps

Operational framework for deploying and managing ML models.

Model Drift

When model performance degrades as data changes over time.

Security, Ethics and Compliance

Bias in AI

Systematic unfairness embedded in models.

AI Governance

Organizational oversight of AI model lifecycle and risks.

Fairness Metrics

Quantitative measures to detect and mitigate bias.

PII (Personally Identifiable Information)

Data that can identify an individual.

Model Watermarking

Techniques to verify model ownership or detect generated content.

Red Teaming

Stress-testing AI models for vulnerabilities or misuse.

Data Privacy

Protection of user information from unauthorized access.

GDPR / DPDP

Regulations governing personal data protection.

Responsible AI

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

Adversarial Attack

Input designed to fool AI models.

AI Applications and Use Cases

Recommender System

Suggests items based on user preferences.

Agentic AI

Autonomous systems that can plan and act toward goals.

Chatbot

Conversational AI system for automated dialogue.

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.

Copilot

AI assistant that augments human work.

Document Intelligence

Extracting structured data from documents.

Synthetic Data

Artificially generated data used for model training.

Semantic Search

Search using meaning rather than exact keywords.

Foundational AI Concepts

Foundation Model

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

Fine-tuning

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

Deep Learning (DL)

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

Machine Learning (ML)

Algorithms that learn patterns from data without explicit programming.

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.

Artificial Intelligence (AI)

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

Transfer Learning

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

Reinforcement Learning (RL)

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

Unsupervised Learning

ML approach where the system identifies patterns in unlabeled data.

Tools, APIs and Frameworks

LangChain

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

Weights & Biases (W&B)

Tool for experiment tracking and model management.

Amazon Bedrock

AWS service offering access to multiple foundation models.

Google Vertex AI

Google Cloud's managed AI platform.

OpenAI API

Interface to access GPT models.

Anthropic Claude

LLM known for safety and long-context reasoning.

TensorFlow

Deep learning framework by Google.

PyTorch

Widely used ML framework developed by Meta.

Hugging Face

Platform for hosting and sharing open-source models.

LlamaIndex

Framework for indexing data sources for RAG systems.

Advanced and Emerging Topics

Tool Use (Function Calling)

Allowing models to interact with APIs and data sources.

Quantization

Compressing models by reducing precision for faster inference.

Chain of Thought (CoT)

Step-by-step reasoning method in LLMs.

AutoGen / CrewAI

Frameworks for orchestrating multiple AI agents collaboratively.

Distillation

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

Sovereign AI

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

Agent Frameworks

Toolkits for building multi-step AI agents.

LoRA (Low-Rank Adaptation)

Efficient fine-tuning technique for large models.

Tree of Thoughts (ToT)

Structured multi-path reasoning for decision-making.

Multimodal Fusion

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

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