AI & Infrastructure Glossary
Technical concepts powering AI retrieval and search systems.
(RAG) Retrieval Augmented Generation
Learn what Retrieval-Augmented Generation (RAG) is, how it works, and why it is fundamental to modern AI search and answer systems.
AI Agents
Learn what AI Agents are, how they work, and how organizations use them to automate tasks, analyze data, and improve workflows across AI-powered systems.
AI Workflows
Learn what AI Workflows are, how they work, and how organizations automate research, analysis, content creation, and decision-making using AI
Agent Chat
Learn what Agent Chat is, how it works, and how organizations use conversational AI interfaces to analyze data, automate tasks, and improve decision-making.
Content Retrieval
Learn what Content Retrieval is, how AI systems retrieve information, and why retrieval quality is essential for AI visibility and answer generation.
Context Window
Learn what a Context Window is, how it works, and why context size affects AI search, retrieval, and answer quality.
Dynamic Retrieval
Learn what Dynamic Retrieval is, how AI systems retrieve information in real time, and why it is essential for AI search and answer quality.
Embeddings
Learn what Embeddings are, how they work, and why they are fundamental to AI search, retrieval, recommendations, and semantic understanding.
Entity Linking
Learn what Entity Linking is, how AI systems connect concepts and entities, and why it matters for AI search and visibility.
Entity Recognition
Learn what Entity Recognition is, how AI systems identify entities, and why entity detection is essential for AI search and visibility.
Fine Tunning
Learn what Fine-Tuning is, how AI models are adapted to specific tasks, and how it differs from retrieval-based AI systems.
Foundation Models
Learn what Foundation Models are, how they work, and why they power modern AI search, assistants, and generative AI systems.
Grounding
Learn what Grounding is, how AI systems use external information, and why grounding is essential for accurate and trustworthy AI answers.
Hallucination
Learn what AI hallucinations are, why they occur, and how grounding, retrieval, and trust signals help reduce them.
Human Feedback
Learn what Human Feedback is, how it improves AI systems, and why human evaluation remains essential for trustworthy AI.
Hybrid Search
Learn what Hybrid Search is, how it combines keyword and semantic retrieval, and why it powers modern AI search systems.
Information Retrieval
Learn what Information Retrieval is, how retrieval systems work, and why information retrieval powers modern AI search and answer engines.
Knowledge Base
Learn what a Knowledge Base is, how AI systems use knowledge repositories, and why knowledge bases are essential for AI search and retrieval.
Knowledge Graph
Learn what a Knowledge Graph is, how AI systems use entity relationships, and why knowledge graphs are essential for AI search and visibility.
Knowledge Retrieval
Learn what Knowledge Retrieval is, how AI systems retrieve information, and why retrieval quality determines AI answer accuracy and visibility.
Language Models
Learn what Language Models are, how they work, and why they power modern AI search, assistants, and generative AI experiences.
Large Language Models (LLMs)
Learn what Large Language Models (LLMs) are, how they work, and why they power modern AI search, assistants, and generative AI experiences.
MCP Server
Learn what an MCP Server is, how Model Context Protocol connects AI agents with tools, and why MCP matters for AI workflows.
Model Alignment
Learn what Model Alignment is, how AI systems are aligned with human preferences, and why alignment is critical for trustworthy AI.
Model Context Window
Learn what a Model Context Window is, how it affects AI performance, and why context size matters for retrieval, reasoning, and AI search.
Multi-step Retrieval
Learn what Multi-Step Retrieval is, how AI systems retrieve information iteratively, and why it improves answer quality and AI search performance.
Multimodal AI
Learn what Multimodal AI is, how it processes different types of information, and why multimodal systems are transforming AI search and discovery.
Named Entities
Learn what Named Entities are, how AI systems identify them, and why entities are fundamental to AI search, retrieval, and visibility.
Natural Language Processing
Learn what Natural Language Processing (NLP) is, how it powers AI search and language models, and why it remains fundamental to modern AI systems.
Prompt Engineering
Learn what Prompt Engineering is, how prompts influence AI systems, and why prompt design matters for generative AI applications.
Query Expansion
Learn what Query Expansion is, how AI systems expand user queries, and why it improves retrieval, AI search, and answer quality.
Query Fan-out
Learn what Query Fan-Out is, how AI search systems expand queries into multiple searches, and why it improves retrieval and AI visibility.
Retrieval Layer
Learn what a Retrieval Layer is, how it works in AI systems, and why retrieval architecture is fundamental to AI search and answer generation.
Retrieval Pipeline
Learn what a Retrieval Pipeline is, how AI systems retrieve information, and why retrieval pipelines are fundamental to modern AI search.
Vector Database
Learn what a Vector Database is, how vector search works, and why vector databases power modern AI search and retrieval systems.
Zero-Shot Learning
Learn what Zero-Shot Learning is, how AI systems generalize to unseen tasks, and why zero-shot capabilities power modern AI search and language models.