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RAG Chatbots Explained: When AI on Your Company Docs Actually Makes Sense

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Retrieval-augmented generation (RAG) is a technique where an AI system first searches a company's own documents for relevant passages before generating an answer, citing the source rather than relying on its pre-trained memory. This approach is gaining traction in enterprise settings, with RAG listed as a requirement in 13.6% of US AI Engineer job postings on Glassdoor as of April 2026, outpacing agents and prompt engineering. The technology is best suited to organisations with large document libraries and high volumes of repetitive queries, such as customer support teams, HR departments, or sales teams navigating large product catalogs. However, experts warn that the hardest challenges are not technical setup but data quality issues like outdated files, poor document structure, and unclear ownership of content updates. Successful deployment also requires a curated set of real questions with verified answers to evaluate whether the bot is responding accurately rather than just confidently.

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