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Retrieval-Augmented Generation chatbot - an AI assistant that answers questions by retrieving and referencing specific content from a defined knowledge source, such as your documentation library.
Retrieval-Augmented Generation chatbot - an AI assistant that answers questions by retrieving and referencing specific content from a defined knowledge source, such as your documentation library.
Many teams document their RAG chatbot setup the same way they handle most internal knowledge: through recorded walkthroughs, onboarding sessions, and architecture review meetings. Someone shares their screen, explains how the retrieval pipeline works, which knowledge sources are connected, and how the system decides what to surface — and that recording gets filed away in a shared drive.
The problem is that a RAG chatbot is only as useful as the documentation it can actually retrieve. If your team's knowledge about configuring, maintaining, or expanding that chatbot lives exclusively in video recordings, it cannot be indexed — which means the RAG chatbot itself cannot reference it. You end up in a frustrating loop: the tool designed to surface answers cannot answer questions about itself because its own setup knowledge is locked in an unwatchable format.
Converting those recordings into structured, searchable documentation changes this directly. Your implementation decisions, retrieval configurations, and knowledge source definitions become text that your RAG chatbot can actually ingest and reference. For example, a recorded session explaining why certain document types were excluded from the knowledge base becomes a retrievable policy your chatbot can cite when users ask why something is missing.
If your team regularly captures processes on video, there is a more direct path to documentation your RAG chatbot can use.
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