Large Language Model

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.

In AI, hallucination refers to when a language model generates confident-sounding but factually incorrect or fabricated information. This occurs because LLMs predict statistically likely text rather than retrieving verified facts. Mitigation strategies include RAG, grounding responses in source documents, structured output validation, and using temperature settings to reduce creative deviation.

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.

ETL (Extract, Transform, Load) is an automated data processing pattern where data is extracted from source systems, transformed into a desired format or structure, and loaded into a target system like a data warehouse. Modern variations include ELT, where raw data is loaded first and transformed in place. ETL pipelines are essential for automating data integration, reporting, and feeding clean data into ML training workflows.

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.

Fine-tuning is the process of further training a pre-trained AI model on a smaller, domain-specific dataset to adapt it for a particular task. Instead of training from scratch, fine-tuning adjusts existing model weights, which is significantly cheaper and faster. Common approaches include full fine-tuning, LoRA (Low-Rank Adaptation), and instruction tuning for aligning model behavior with specific requirements.