Chain of Thought

An embedding is a dense numerical vector representation of data — such as text, images, or code — in a high-dimensional space where semantically similar items are positioned closer together. Embeddings are fundamental to semantic search, recommendation systems, and RAG pipelines. They are generated by specialized models and typically stored in vector databases for efficient similarity lookups.

A large language model (LLM) is a deep learning model trained on massive text datasets to understand and generate human-like text. LLMs like GPT, Claude, and LLaMA power chatbots, code assistants, and content generation tools. They work by predicting the next token in a sequence based on learned statistical patterns across billions of parameters.

n8n is an open-source workflow automation platform that lets you connect APIs, services, and databases through a visual node-based editor. Unlike proprietary alternatives like Zapier, n8n can be self-hosted, giving full control over data and execution. It supports hundreds of integrations, custom JavaScript/Python code nodes, and AI agent workflows, making it popular among developers who need automation with flexibility and transparency.

Generative AI is a category of artificial intelligence that creates new content — text, images, code, music, or video — rather than just analyzing or classifying existing data. Powered by architectures like transformers and diffusion models, generative AI has transformed software development with tools like GitHub Copilot, Claude, and Cursor. It represents a shift from AI as a classification tool to AI as a creative collaborator.

A vector database is a specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings. They enable fast similarity search, which is critical for RAG systems, recommendation engines, and semantic search applications. Popular vector databases include Pinecone, Weaviate, Qdrant, and pgvector for PostgreSQL.

A context window is the maximum amount of text (measured in tokens) that an LLM can process in a single interaction, encompassing both the input prompt and the generated output. Larger context windows allow models to handle longer documents, maintain extended conversations, and reason over more information at once. Context window sizes have grown rapidly — from 4K tokens in early GPT models to over 1M tokens in current models like Claude.