Artificial intelligence is reshaping industries, redefining workflows, and — let’s be honest — inventing a whole new vocabulary along the way. Spend even a few minutes reading about AI and you’ll encounter terms like LLMs, RAG, RLHF, and AGI. For many, it can feel like everyone else is in on a secret language. This glossary is designed to change that. It is updated regularly as the field evolves, so consider it a living document — much like the systems it describes.
Core concepts everyone should know
AGI (Artificial General Intelligence) is a term that means different things to different people, even among experts. Generally, it refers to AI that matches or exceeds human ability across a wide range of tasks. OpenAI’s Sam Altman has described it as the ‘equivalent of a median human that you could hire as a co-worker,’ while DeepMind defines it as ‘AI that’s at least as capable as humans at most cognitive tasks.’ The lack of a single definition reflects how early we still are in this journey.
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AI agents are tools that use AI to perform multi-step tasks on your behalf — filing expenses, booking travel, or writing code. Unlike a basic chatbot, an agent can act autonomously across different systems. The infrastructure is still being built, but the concept implies an autonomous system that may draw on multiple AI models to complete a goal.
Large language models (LLMs) power popular assistants like ChatGPT, Claude, Gemini, and Meta’s Llama. These are deep neural networks with billions of parameters that learn the relationships between words by processing vast amounts of text. When you prompt an LLM, it generates the most likely pattern that fits your request.
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How AI learns and improves
Training is the process of feeding data to a model so it can learn patterns and generate useful outputs. It’s expensive and requires enormous amounts of data. Fine-tuning takes a pre-trained model and further trains it on specialized data for a specific task — a common approach for startups building domain-specific products.
Reinforcement learning trains AI through trial and error, rewarding correct answers. Think of it like training a pet with treats, except the ‘treat’ is a mathematical signal. Techniques like RLHF (reinforcement learning from human feedback) are now central to making models more helpful and safe.
Distillation is a teacher-student technique where a large model’s knowledge is extracted to train a smaller, faster model. This is likely how OpenAI developed GPT-4 Turbo. While used internally by all AI companies, distillation from a competitor typically violates terms of service.
Understanding AI outputs and limitations
Hallucination is the industry term for when AI models generate incorrect information. It’s a major quality problem, especially for sensitive queries like medical advice. Hallucinations arise from gaps in training data and are driving interest in more specialized, domain-specific models.
Tokens are the basic building blocks of human-AI communication. They break text into bite-sized units a model can process. In enterprise settings, tokens also determine cost — most AI companies charge on a per-token basis.
Inference is the process of running a trained model to make predictions. It’s distinct from training and can happen on various hardware, from smartphones to cloud servers with high-end chips.
Key technical mechanisms
Chain of thought reasoning involves breaking down a problem into smaller intermediate steps, improving accuracy especially for logic and coding tasks. It takes longer but produces better results.
Diffusion is the technology behind many image and music generators. Inspired by physics, these systems learn to reverse a process of adding noise to data, enabling them to generate new content from random noise.
Weights are numerical parameters that determine how much importance the model assigns to different features in the data. They are core to how AI models learn and make decisions.
Why this matters now
The AI industry is moving fast, and the language used to describe it is evolving just as quickly. Understanding these terms isn’t just about keeping up with conversation — it’s about making informed decisions about the tools we use, the products we buy, and the policies we support. Whether you’re a developer, a business leader, or simply a curious reader, having a clear mental model of these concepts helps separate genuine innovation from marketing hype.
Conclusion
This glossary will continue to evolve as the field advances. We will add new terms and update existing definitions to reflect the latest understanding. The goal is simple: to make AI accessible and understandable for everyone, not just the engineers building it.
FAQs
Q1: What is the difference between AI, machine learning, and deep learning?
AI is the broad field of creating machines that can perform tasks requiring human intelligence. Machine learning is a subset of AI where systems learn from data. Deep learning is a further subset using multi-layered neural networks inspired by the human brain.
Q2: Why do AI models ‘hallucinate’?
Hallucinations occur when a model generates information that is incorrect or fabricated. This happens because the model is predicting patterns based on its training data, and gaps or biases in that data can lead to confident but wrong outputs.
Q3: What does ‘open source’ mean in AI?
Open source AI models have their underlying code and often their weights made publicly available for anyone to use, inspect, or modify. Meta’s Llama is a prominent example. This contrasts with closed-source models like OpenAI’s GPT, where the code is private.
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