SharePoint AI Playlist · Video 17
Why AI Fails Your Document Library
AI can summarize a contract in seconds, but ask it to find every contract missing a termination clause and it falls apart. Metadata is the missing layer — extracted once at upload, queried forever.
Practice — test & drill what you've learned
Curriculum — read the guides in order
01
01 · Foundations
Where AI Falls Apart on Document LibrariesSingle docs vs. library-wide questions
Why "summarize this contract" works but "find all contracts missing a termination clause" doesn't — and what metadata extraction at upload changes.
extraction columns search limits 4 cards
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02
02 · Applied
Three Reasons to Extract Up FrontScale, consistency, and deterministic filters
Scale (the token bill), consistency (locked answers), and unanswerable-without-metadata questions. Each one alone justifies the extraction pipeline.
scale consistency deterministic 4 cards
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03
03 · Advanced
Compound Queries & BI on LibrariesTwo-step questions, dates, and where RAG breaks
Once metadata is in place, you can chain "define the universe" with "analyze across it," normalize dates, and run real business-intelligence queries on the library.
compound queries dates RAG limits 4 cards
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How to use this site
Start with Guide 01 — Foundations ; it frames the problem and the fix.
Guide 02 — Applied covers the three reasons to extract metadata.Guide 03 — Advanced shows compound queries and where standard RAG breaks.Drill the 12 Flashcards — shuffle, filter by guide, or flip all at once.