AI Concepts

Understand the core components that power modern artificial intelligence systems.

Modern AI systems are built from interconnected components. Large language models generate responses. Embeddings convert data into numerical representations. Vector databases store and search those representations at scale. And retrieval systems connect everything to real-world information. Understanding how these pieces fit together is essential for anyone building, deploying, or making decisions about AI.

Core Concepts
01 · Parent Concept
Large Language Models (LLMs)
Neural networks trained on massive text datasets that can understand, generate, and reason about human language. The technology behind ChatGPT, Claude, and Gemini.
02 · Connector
Embeddings
Numerical representations that capture meaning in mathematical form, enabling AI to search, compare, and reason about information by semantic similarity.
03 · Infrastructure
Vector Databases
Specialised data systems that store and search high-dimensional vectors, enabling AI applications to find relevant information in under 50 milliseconds.
04 · Application
Retrieval-Augmented Generation (RAG)
The architecture that grounds language model responses in current, verified information by combining retrieval with generation.
05 · Autonomy
AI Agents
Autonomous software systems that combine LLM reasoning with tool execution, memory, and feedback loops to complete multi-step tasks independently.

How these concepts connect

Large language models generate responses, embeddings convert data into vectors, vector databases store and retrieve them, retrieval systems connect everything to real-world information, and AI agents orchestrate these components to take autonomous action. Together they form the standard architecture for enterprise AI deployment in 2026.

LLMs
Embeddings
Vector DBs
RAG
Agents
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