A Beginner's Guide to AI Terminology: AGI, LLM, Agents, and Other Key Terms Explained

The Proliferation of AI Jargon and Its Background

The rapid development of artificial intelligence (AI) is not only transforming our world but also creating an entirely new language to describe it. In just a few minutes of reading about AI, you might encounter terms like LLM (Large Language Models), RAG (Retrieval-Augmented Generation), and RLHF (Reinforcement Learning with Human Feedback), which can baffle even seasoned tech professionals. This article aims to clear up the confusion by providing simple explanations of some of the most important AI-related terms. As this field continues to evolve, the glossary of terms, much like the AI systems they describe, is a “living document” that requires constant updates.

What is AGI (Artificial General Intelligence)?

AGI, or Artificial General Intelligence, is a somewhat ambiguous term. Generally, it refers to an AI capable of outperforming the average human in most, if not all, tasks. OpenAI CEO Sam Altman describes AGI as “something you can hire as a colleague, equivalent to a median human.” In contrast, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind has a slightly different understanding, referring to AI that is “at least as capable as humans in most cognitive tasks.” While even leading researchers find the concept challenging to pin down, the main difference lies in the scope: current AI systems are specialized for specific tasks, whereas AGI would have the ability to tackle a wide range of problems, much like a human.

The Role and Potential of AI Agents

AI agents go beyond basic AI chatbots to act as tools that execute a series of tasks on behalf of users. These tasks can range from expense reporting and booking tickets or restaurant reservations to coding and software maintenance. However, this emerging field has many dynamic aspects, and the term “AI agent” can mean different things to different people. The infrastructure needed to realize their full potential is still under development. At its core, the concept refers to autonomous systems that leverage multiple AI technologies to perform multi-step tasks. In the future, AI agents are expected to become a central technology in automating increasingly complex operations.

The Importance of API Endpoints

API endpoints are like the “buttons” behind software that allow other programs to interact with it. Developers use these interfaces to build integrations. For instance, one application might pull data from another, or an AI agent might directly control third-party services without human intervention. Many smart home devices and connected platforms feature these hidden buttons, even though end-users don’t see or interact with them directly. As AI agents grow more sophisticated, they will increasingly identify and utilize these endpoints, opening up powerful—and sometimes unexpected—opportunities for automation.

The Concept of Chain-of-Thought Reasoning

When asked a simple question, the human brain can sometimes answer without much thought—for example, “Which is taller, a giraffe or a cat?” However, arriving at the correct answer often requires intermediate steps, perhaps even using pen and paper. For instance, if a farmer has chickens and cows, with a combined total of 40 heads and 120 legs, solving this would involve setting up an equation to determine there are 20 chickens and 20 cows. In the context of AI, chain-of-thought reasoning refers to a method where large language models break down complex problems into smaller, manageable steps. This allows the models to produce more accurate and logical responses, moving beyond simple pattern matching.