Posts tagged "explainer."
Follow one document through the entire pipeline — upload, chunking, embedding, weeks of sitting idle, and the moment it finally answers a real question.
A worked example of how a single sentence gets broken into tokens before an AI model ever reads it — and why the split isn't where you'd guess.
Plain-English translations of the AI terms that show up everywhere and get explained nowhere — agentic, multimodal, fine-tuning, guardrails, and more.
Everything an AI model 'knows' comes from what it was trained on. Here's what that data actually is, and why it can never fully cover what you need it to.
Parameter count gets used as shorthand for how 'smart' an AI model is. Here's why that shorthand breaks down for most real-world questions.
Admitting uncertainty isn't a natural behavior for an AI model — it has to be deliberately engineered in. Here's why, and what it actually takes.
AI models don't have a built-in way to know what they don't know. Here's what actually happens when a question falls outside what a model was trained on.
Most 'open source' AI models aren't open source in the traditional software sense. Here's what's actually being shared, and what isn't.
Ask two different AI models the same question and you can get two different answers. Here's why — and why it matters less than you'd think for a grounded chatbot.
A plain explanation of the 'temperature' setting behind AI models — what it actually changes, and why the most predictable setting isn't always the most accurate one.
A millisecond-by-millisecond breakdown of what actually happens between a visitor hitting send and an AI chatbot's answer appearing on screen.
A plain explanation of similarity search — how an AI chatbot finds the right chunk of your docs, and why it works by meaning, not keywords.
What prompt injection is, why it's a real risk for AI chatbots, and how grounding answers in scoped, trusted content reduces the attack surface.
A plain explanation of chunking and embeddings — the two steps that turn a document into something an AI chatbot can search and answer from.
What separates an AI answer grounded in your documentation from one that's confidently made up — and why the distinction matters for support.
A general-purpose AI model dropped into a support widget doesn't know your product. Here's why grounding it in your own docs is what matters.
What a token actually is, why AI pricing is measured in them, and why a chatbot can seem to 'forget' things you said earlier in a conversation.
A plain-English breakdown of what actually drives AI chatbot costs — tokens, embeddings, storage — and how to think about build-it-yourself versus buying.
A plain-language explanation of retrieval-augmented generation — the technique behind AI chatbots that answer from your own docs instead of guessing.
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