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A knowledge base chatbot answers questions from your documentation instead of the model's imagination — the difference between a useful assistant and a liability. The architecture is standard (retrieval plus generation); the quality is decided by your content and your fallback behavior.
The bot can only answer what the docs answer. Fill the gaps your support tickets reveal, delete outdated pages, and make each page answer one question well. Every hour here improves every answer later.
Split content along its natural structure (one topic, one procedure) so retrieved chunks are self-contained. Arbitrary fixed-size splitting cuts answers in half and is the most common cause of wrong responses.
Configure the bot to answer only from retrieved content and cite the source page for every claim. Citations let users verify, and they let you trace every bad answer to either a retrieval miss or a content gap.
When retrieval finds nothing relevant, the bot should say so and route to a human or a search — never improvise. A bot that admits ignorance keeps trust; one that guesses destroys it with a single confident wrong answer.
Read a sample of actual conversations each week: unanswered questions become doc backlog, wrong answers get traced to chunking or content. The transcript loop is where a mediocre bot becomes a good one.
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