Cut Through the AI Noise: 10 Terms That Matter

As AI tools become more integrated into the way we work, create, and make decisions, it's easy to feel like you're catching up with a conversation that's already miles ahead. While you don't need to be a data scientist to use AI well, it does help to understand the language if you want to use AI with clarity, not just curiosity.

Here are 10 AI terms that are worth knowing:

1. Generative AI
AI that creates new content such as text, images, music, or video instead of just analysing existing data.
Think ChatGPT, Claude, and tools that write emails, create images or generate voiceovers.

2. Multi-modal AI
AI that can understand and work across more than one type of input, like text, images, audio, or video, and combine them in useful ways.
Examples: Upload a photo and ask questions about it or upload an audio clip and get a written summary.

3. Machine Learning (ML)
The part of AI that enables systems to learn patterns from data and make predictions or decisions without being explicitly programmed. It’s how AI improves by learning from examples.

4. Large Language Model (LLM)
A type of AI trained on massive amounts of text to understand and generate human-like language.
Examples: GPT-4, Claude, Gemini. These power most AI chat tools.

5. Natural Language Processing (NLP)
The technology that allows AI to understand, interpret, and respond to human language, whether it’s spoken or written.
Used in chatbots, transcription, translation, and search.

6. Prompt-Chaining
A method of linking multiple AI prompts together to complete a multi-step task.
Example: Ask AI to summarise a document, then turn the summary into a LinkedIn post, then translate it.

7. Hallucination
When AI confidently generates incorrect or made-up information.
Important to watch for, especially when using AI for research or analysis.

8. Token
The building blocks AI uses to understand and process language. A token might be a word, part of a word, or even punctuation.
Models have token limits. The longer your input, the more tokens used.

9. Embedding
A way of turning words or phrases into numbers so that AI can understand relationships between them, like knowing “king” and “queen” are similar.
Used in search, recommendations, and document matching.

10. Human-in-the-loop
A setup where AI is supported by human oversight, either to check outputs, make final decisions, or correct errors.
Especially common in sensitive areas like hiring, healthcare, or finance.