How good a support chatbot is depends less on the model than on the material behind it. This section is about that material and the process around it: which requests to start with, how to prepare a knowledge base and keep it current, how to reduce repeated questions, what to say to an unhappy customer, and when the bot should stop and call a person in.
The articles are written for support leads and whoever owns the knowledge base. The knowledge base page shows where the bot's answers come from, and analytics shows how they are measured.
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A reusable test matrix across happy paths, ambiguity, multilingual input, unsupported requests, escalation and abuse — with what a pass actually looks like.
Ownership, update SLAs, approval flows and change logs — the operating model that stops a chatbot drifting away from reality, plus the monthly routine that catches drift.
Why most wrong answers are retrieval failures rather than model failures — chunking, ranking and grounding explained in business terms, with an evaluation checklist.
A pre-launch audit for completeness, contradictions, freshness and escalation content — the four failure modes that make a bot look unreliable on day one.
Language detection, localised knowledge, register and escalation — why translating the material is the smallest part of running a chatbot in several languages.
Why chatbots invent answers, what grounding actually is, how to prepare your material so retrieval works, and how to test for hallucinations before customers do.
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