AI buzzwords for beginners - terminology cheat sheet

It all started with AI

Were you ever confused by the terminology in a conversation about AI? Felt lost after hearing words like “LLM,” “hallucination,” or “RAG”? Don’t worry — you’re not alone!

The good news? You don’t need to be a data scientist to sound like you know what’s going on. In this guide, we’ll go over the essential AI buzzwords that keep popping up in conversations, product updates, and news headlines.

Data Science & AI

If you’re interested in IT or work in a related field, you’ve likely encountered numerous ads for data science courses, internships, and events. But what is data science and how is it related to AI?

Data is the foundation of AI systems. The duties of data science are collecting, cleaning, and analyzing massive amounts of data. Data scientists create the patterns and insights that AI systems rely on to learn and make decisions. Without high-quality data and rigorous analysis, even the smartest AI algorithms can’t deliver meaningful results.

The big picture AI terms

Here are the core AI terms. Knowing these will help you make sense of articles, meetings, and tech talks.

Artificial Intelligence (AI) - A type of AI where computers improve by learning from data instead of following fixed instructions.

Machine Learning (ML) - A major subset of AI: systems that improve their performance on tasks by learning from data, rather than following explicit hand-coded instructions.

Example: If you show a computer thousands of pictures of cats and dogs, it can learn to tell them apart on its own.

Deep Learning (DL) - A more advanced form of machine learning that uses layered approach to recognise patterns, such as faces in photos or meaning in text.

Synonyms for AI

AI goes by many names depending on who’s talking and what context it’s used in. While “Artificial Intelligence” is the most common term, you might also encounter alternatives:

Cognitive Computing - Simulating human thinking, usually in business tools.

Intelligent Automation - AI paired with automation that makes decisions without human input.

Machine Intelligence - A broad term for systems that can learn and reason.

Smart Technology / Smart Systems - The consumer-friendly naming of AI, like “smart” apps or devices.

Computational Intelligence - A name for AI systems inspired by nature.

People can use different words for AI, but they mostly mean the same thing: computers that can think or learn.

The basic terminology

When people talk about AI, they often use words that sound complicated. In reality, most of them describe simple ideas. Below are some of the terms explained in plain language.

Algorithm - A set of instructions that tells the computer how to solve a problem.

Neural network - A web of virtual “neurons” inspired by the human brain that helps models spot patterns in data.

Parameters - The internal settings of the AI, that can be adjusted while learning.

Training data - The examples AI learns from, such as labeled images or text.

Forward chaining - Reasoning method that starts from data and moves forward to draw conclusions.

Backwards chaining - Reasoning method that starts from a goal and figures out what needs to be true to reach it.

More AI Buzzwords

Large Language Model (LLM) - A type of model trained on tons of text (books, websites, chats) so it can talk and write like a human. These power many recent AI tools.

Generative AI (GenAI) - AI that creates new things instead of analysing existing data. For example: text, pictures, songs, or code.

Hallucination - Instance when AI makes up something that sounds real but isn’t true.

Multimodal model - An AI that can handle more than one kind of data. For example reading text and looking at pictures at the same time.

Prompt (engineering) - The input or instruction given to AI. Prompt engineering is a process of refining instructions, to get the best possible output.

Retrieval-augmented generation (RAG) - A technique when AI looks up real information (like from documents or a database) before answering you, so it’s more accurate.

Bias - Term that describes unfairness in model output. When AI gives unfair or one-sided results because the data it learned from wasn’t balanced.

Explainability / XAI - How clearly we can understand why an AI made a certain decision.

Responsible AI / Governance - Frameworks, rules, and practices designed to make sure AI is ethical, transparent, and safe to use.

Emergent behaviour - When AI starts doing something unexpected. Not explicitly trained, but learned along the way.

Quick Glossary (10 Must-Know Terms)

A recap of the most-used AI terms:

1. AI (Artificial Intelligence) - Machines that simulate human intelligence.

2. ML (Machine Learning) - AI systems that learn from data.

3. Deep Learning - Advanced ML using many-layered neural networks.

4. LLM (Large Language Model) - Language-trained “giant” models, capable of generating or understanding text.

5. Generative AI - AI that creates new content (for example: images, music, text).

6. Prompt / Prompt engineering - The instruction you give to an AI model. Creation of said instruction.

7. Hallucination - When an AI gives false or made-up information.

8. Algorithm - A set of instructions that tells the computer how to solve a problem.

9. Bias - Unfair or inaccurate results due to input or training data issues.

10. Parameters - The internal settings of the AI, that can be adjusted while learning.

Wrapping up

AI might seem complicated at first. In the end, it’s just a set of tools that learn from data to help us solve problems. The more familiar you are with the ideas we’ve covered, the easier it becomes to understand what AI can actually do. Knowing these AI terms helps you follow industry trends, compare tools, and communicate with technical teams.

Once you understand these words, you’ll start to see how the pieces fit together and remember more of what you read about AI. At this point the technology stops feeling mysterious and starts feeling useful. That’s where the real power begins.