Posts tagged "rag."
Follow one document through the entire pipeline — upload, chunking, embedding, weeks of sitting idle, and the moment it finally answers a real question.
The same customer question, answered three different ways — by an ungrounded AI, a poorly-grounded chatbot, and a well-grounded one — and what actually separates them.
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.
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 single, realistic support conversation annotated turn by turn — what's actually happening behind each reply, from retrieval to escalation.
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.
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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