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RAG & search
Anchoring answers to real sources: RAG, grounding, semantic search.
3 · Articles on this topic
Semantic Search: How AI Understands Meaning, Not Just Words
For decades, searching meant matching words: you type a term, the system returns the documents that contain that exact term. Semantic search changes the rules. It doesn't chase words, it chases meaning: it grasps that a question phrased one way and an answer written another way can be about the same thing even without sharing a single word. Behind this leap sits a different way of representing language, one that lets AI find what you mean and not just what you type. Understanding how it works helps you read the systems you use every day, and see why comparing several perspectives is still the step that matters.
Embeddings: how an AI model organizes meaning
When you type a sentence into an AI system, something invisible happens before it even answers: your words are turned into numbers, into coordinates inside a space. This step has a name, embedding, and it is one of the building blocks under almost everything AI can do today, from search by meaning to the ability to connect distant ideas. This is not an insider detail: grasping, in broad strokes, how a model lays concepts out in this space helps you see why it nails some things and fails others. The model does not know the world the way you do, but it holds a map of meaning, and that map explains a lot of its behavior. It is worth looking inside, without jargon, so you can use AI with more awareness.
Google I/O 2026: Implications for AI Developers
A multimodal search box, AI Mode as the default for over a billion users, and Gemini Spark for $100 a month. I/O 2026 isn’t just an announcement—it’s confirmation that the rules of the game have changed for those building with AI. Here’s what that means in practice.
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