RAG (Retrieval-Augmented Generation)
A technique where the model first retrieves relevant documents from an external knowledge base, then uses them as context to generate its answer. It reduces hallucinations and lets the model answer questions about recent or highly specialized data.
See also
LLM (Large Language Model)
A neural network trained on massive amounts of text that can understand and generate human language. Examples include GPT, Claude, Gemini, and Grok. It powers most modern chatbots and AI assistants.
Transformer
The neural network architecture introduced in the 2017 paper "Attention Is All You Need" that underlies almost every modern LLM. Its key idea is the attention mechanism, which lets the model weigh the relationships between all words in a text simultaneously.
Prompt Engineering
The art and practice of crafting prompts to an AI so that it produces the most accurate and useful response. It includes phrasing the task clearly, adding examples and context, and specifying the desired output format.
Fine-tuning
Additional training of an already-trained model on a narrow dataset to adapt it to a specific task or style — for example, legal documents or a brand's tone — without training the model from scratch.
AI Hallucination
A situation where a model confidently states false or entirely made-up information as fact — for example, a nonexistent quote or fabricated statistic. It's the main reason AI-generated answers should always be fact-checked.
Context Window
The maximum amount of text (measured in tokens) a model can "see" at once — including the prompt itself, the conversation history, and any attached files. The larger the window, the longer the documents the model can process in one go.