If you want to understand how an AI model turns your words into something a machine can actually work with, three ideas do almost all of the explaining: tokens, embeddings, and vectors. In one chain: an AI breaks your text into tokens (small chunks), then…
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Artificial intelligence, machine learning, and deep learning are not three competing things you choose between. They are three nested ideas, each one a more specialised part of the one before it. AI is the big outer goal: machines doing things that would normally need human…
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RAG, retrieval-augmented generation, is a way of making an AI model answer from a specific, trusted set of documents instead of relying only on what it absorbed during training. Before the model writes its answer, the system goes and fetches the most relevant material from…
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A hallucination is when an AI model produces an answer that sounds completely plausible and is completely false. Not a glitch, not a bug in the usual sense, but a confident, fluent, well-formed answer that happens not to be true. Understanding why this happens is…
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MCP, the Model Context Protocol, is an open standard that gives AI models a single, consistent way to connect to external tools and data. Instead of building a custom integration for every combination of AI model and service, you build one MCP connection and any…
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A large language model is a system that has been trained to do one deceptively simple thing: predict the next word. Show it a stretch of text, and it guesses what most likely comes next, then it guesses again, and again, one piece at a…
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An AI agent is a system that takes a goal, breaks it into steps, uses tools to carry those steps out, and decides on its own what to do next based on what just happened. That last part is the whole difference. A chatbot answers…
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