A knowledge base is the curated document store that a RAG system retrieves from. It can be as simple as a folder of Markdown files or as complex as a live-synced index of your entire Confluence, Notion, Salesforce, and document repository.
What goes in matters enormously. Garbage in, garbage out: poorly written, duplicated, or outdated documents degrade retrieval quality regardless of how sophisticated your search is. Curation tasks worth investing in: deduplication, freshness policies (auto-archive documents older than X), source metadata (department, product, author), and format normalization.
A knowledge base is not just a technical system — it's an information architecture problem. The teams that get the most out of AI knowledge assistants spend as much time on content governance as on the retrieval pipeline.
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Knowing the term is one thing. Shipping it is another.
We do two-week AI Sprints — one term, one workflow, into production by Day 10.