AI & ML Glossary

AI moves fast and the terminology moves faster. This glossary covers the core concepts you'll encounter in AI engineering job postings, interviews, and technical discussions — from foundational architectures like transformers to applied techniques like RAG and fine-tuning. Each entry explains how the technology works, why it matters for your career, and what salary premium it commands in today's job market.

All Terms

AI Safety (AI Safety & Alignment)

The field of research and engineering focused on ensuring AI systems behave as intended, avoid harmful outputs, and rema...

Agentic AI (AI Agents)

AI systems that can autonomously plan, reason, and execute multi-step tasks. Agents use LLMs as reasoning engines combin...

Computer Vision (Computer Vision)

A field of AI that enables machines to interpret and understand visual information from images and video. Computer visio...

CrewAI (CrewAI)

An open-source framework for orchestrating multiple AI agents that collaborate to complete complex tasks. CrewAI enables...

Diffusion Models (Diffusion Models)

A class of generative AI models that create data (images, audio, video) by learning to reverse a gradual noising process...

Embeddings (Vector Embeddings)

Numerical representations of text, images, or other data in high-dimensional vector space. Embeddings capture semantic m...

Feature Store (Feature Store)

A centralized platform for storing, managing, and serving machine learning features. Feature stores ensure consistent fe...

Fine-tuning (Model Fine-tuning)

The process of training a pre-trained LLM on a smaller, domain-specific dataset to adapt it for particular tasks or indu...

Foundation Model (Foundation Model)

A large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, a...

Inference Optimization (LLM Inference Optimization)

Techniques for making AI model inference faster and cheaper in production, including quantization, batching, caching, di...

Knowledge Graph (Knowledge Graph)

A structured representation of facts and relationships between entities, stored as a network of nodes and edges. Knowled...

LLM (Large Language Model)

A neural network trained on massive text datasets that can generate, understand, and reason about text. LLMs like GPT-4,...

LangGraph (LangGraph)

A framework built on top of LangChain for building stateful, multi-actor AI agent applications using graph-based workflo...

LoRA (Low-Rank Adaptation)

A parameter-efficient fine-tuning technique that adds small trainable matrices to a frozen pre-trained model, enabling a...

MLOps (Machine Learning Operations)

The set of practices for deploying, monitoring, and maintaining ML models in production. MLOps combines ML, DevOps, and ...

Model Registry (Model Registry)

A centralized repository for storing, versioning, and managing machine learning models throughout their lifecycle. Model...

Multimodal AI (Multimodal Artificial Intelligence)

AI systems that can process and generate multiple types of data simultaneously, such as text, images, audio, and video. ...

NLP (Natural Language Processing)

A branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP encompasses ever...

Prompt Engineering (Prompt Engineering)

The practice of designing and optimizing input prompts to get desired outputs from large language models. It involves un...

Quantization (Model Quantization)

A technique for reducing the precision of model weights from 32-bit or 16-bit floating point to smaller formats (8-bit, ...

RAG (Retrieval-Augmented Generation)

A technique that enhances LLM responses by retrieving relevant documents from a knowledge base before generating an answ...

RLHF (Reinforcement Learning from Human Feedback)

A training technique where human preferences are used to guide model behavior. Evaluators rank model outputs, and this f...

Semantic Search (Semantic Search)

Search that understands the meaning of queries rather than just matching keywords. Semantic search uses embeddings and v...

Transformers (Transformer Architecture)

The neural network architecture behind modern LLMs. Transformers use self-attention mechanisms to process sequences in p...

Vector Database (Vector Database)

A database designed to store, index, and query high-dimensional vector embeddings efficiently. Vector databases enable s...