A Guide to Essential AI Terminology: From LLMs to Opaque Recurrence

The AI industry moves with such speed that its vocabulary has become its own frontier. Terms like LLMs, RAG, RLHF, and newer concepts like "opaque recurrence"—the reasoning approach in OpenAI's latest model—now dominate pitch meetings, research seminars, and product discussions. For those building, investing, or simply trying to stay informed, this terminology gap can feel intimidating.

This overview assembles plain-language explanations of the AI terminology you're most likely to encounter, organized as a living reference that tracks how the field evolves.

Core Concepts in AI Systems#

Artificial General Intelligence (AGI) describes AI systems that outperform most humans at a broad range of tasks—though the exact definition remains contested among researchers. OpenAI's Sam Altman framed it as the equivalent of a capable co-worker, while OpenAI's formal charter defines it as "highly autonomous systems that outperform humans at most economically valuable work." Google DeepMind uses yet another benchmark: AI at least as capable as humans on most cognitive tasks.

Large Language Models (LLMs) power today's familiar AI assistants—ChatGPT, Claude, Gemini, Llama, and others. They are deep neural networks containing billions of numerical parameters that map relationships between words and phrases, essentially learning language patterns encoded in books, articles, and transcripts. When prompted, they generate the most probable sequence of language that matches your input.

AI Agents are autonomous tools that move beyond simple chatbots to execute multi-step tasks on your behalf—filing expenses, booking reservations, or writing and maintaining code. The space remains emergent, and infrastructure to support these capabilities is still evolving, but the core idea centers on systems capable of drawing multiple AI tools together to accomplish complex, autonomous workflows.

How AI Models Learn and Improve#

Deep Learning is a branch of machine learning that uses multi-layered artificial neural networks inspired by the brain's structure. Unlike simpler approaches, deep learning algorithms automatically identify important features in data rather than requiring engineers to define them. This self-improving capacity allows systems to refine outputs through error feedback, though it demands large datasets (millions of examples) and lengthy training periods, raising development costs.