AI vocabulary explained: From LLMs to Guardrails, key terms you should know
As AI reshapes industries and global conversations intensify, here’s a simple guide to key AI terms including LLMs, generative AI, guardrails, algorithms, AI bias, hallucinations, prompts and tokens.
Artificial Intelligence is rapidly reshaping industries, workplaces, and everyday digital life. It has emerged as one of the most transformative technologies and a focal point of global conversations. As discussions around AI intensify ahead of the major summit set to be hosted in New Delhi, here is a straightforward guide to some frequently used AI terms making headlines — and what they mean.
What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) refers to the simulation of human intelligence by machines. In simple terms, it involves systems designed to perform tasks that typically require human intelligence — such as understanding language, recognising images, making decisions, solving problems, and increasingly, creating content like text, music, and videos.
At its core, AI enables machines to learn from data. Unlike conventional software that is programmed step-by-step for every scenario, AI systems are trained on large volumes of data to detect patterns, make predictions, and improve over time.
Large Language Model (LLM)
A Large Language Model (LLM) is a type of AI model trained on vast amounts of text data — including books, websites, and articles — to understand and generate human-like language.
LLMs power chatbots, writing assistants, coding tools, and search summaries. They function by predicting the next word in a sequence based on patterns learned from massive datasets. In short, an LLM specialises in language tasks.
Prominent examples include Grok, GPT-4o, Claude 4, Gemini 2.5, Llama 4, and DeepSeek-R1.
Generative AI
Generative AI refers to AI systems that can create new content — including text, images, music, code, and video — in response to prompts.
This category includes text generators (often powered by LLMs), image and video models, voice synthesis tools, and music generators. These systems can summarise reports, write code, compose music, design logos, create marketing copy, generate product descriptions, produce social media posts, build presentations, create synthetic voices, generate realistic visuals, and simulate customer service interactions.
Use cases of AI
A “use case” describes how AI is applied in real-world scenarios. It refers to the practical impact of AI across industries.
Common AI use cases include fraud detection in banking, personalised recommendations on OTT platforms, agricultural tools analysing soil and weather data, healthcare diagnostics, and drug discovery.
Algorithm
An algorithm is a defined set of rules or instructions that tells a computer how to process data and make decisions. Algorithms are the foundational building blocks of AI systems.
AI Guardrails
AI guardrails are safeguards built into AI systems to ensure they operate safely, ethically, and within defined limits.
These safeguards are designed to prevent harmful, biased, illegal, or inappropriate outputs. They align AI behaviour with laws, policies, and human values. Guardrails may include content filters, safety policies, and bias mitigation mechanisms.
AI Bias
AI bias refers to systematic errors in AI outputs caused by skewed training data, flawed assumptions, or design limitations. Bias can lead to unfair or inaccurate results.
AI Hallucination
An AI hallucination occurs when an AI system generates information that appears plausible and convincing but is factually incorrect or entirely fabricated.
Prompt
A prompt is the input or instruction given to a generative AI system to produce a response. The quality and clarity of the prompt can significantly influence the output.
Token
A token is a unit of text, such as a word, sub-word, or character, that an AI model processes during training and while generating responses.
As AI continues to evolve and shape global discussions, understanding these key terms can help decode conversations and better navigate the rapidly changing technological landscape.