TechnicalAI glossary
Embedding
The transformation of a text (or an image) into a list of numbers that captures its meaning. Two pieces of content that are close in meaning have numbers that are close, which is the basis of semantic search and RAG.
In other words
Give GPS coordinates to ideas: neighbouring ideas sit side by side on the map.
Alyriaz articles that mention it
- RAG or fine-tuning: which one should you choose to connect an AI to your data?
To adapt an AI to your data, two approaches are at odds: RAG and fine-tuning. We explain the difference, when to use one or the other, and why most projects pick the wrong method.
- Why nobody really knows what's going on inside an AI
The opacity of AI models is not a commercial choice but a property of their construction. We explain why interpretability is so difficult, what researchers are nonetheless able to observe, and what this limitation implies.
- Does an AI need to understand to be useful?
AIs perform tasks that once seemed to require understanding, without us being able to say they understand anything at all. We explore what this reveals about the nature of understanding itself, and why the question matters in practical terms.
- What is an "embedding"? The technique that lets a machine understand that "doctor" and "physician" mean the same thing.
An embedding transforms text into a list of numbers that encodes its meaning. We explain this key concept, how it enables semantic similarity search, and why it is the invisible engine behind most enterprise document assistants.
- What is a token? And why it is probably the most important concept for understanding AI
If you read about or use AI in 2026, you come across this word constantly: token. Token limits, price per token, context in tokens. And yet hardly anyone actually explains it to you. To understand what is going on inside an AI, understanding tokens is probably the best place to start. And it is actually simple, provided someone takes 5 minutes to explain it properly.