LANDMARK · 8 MIN · BUILD
Retrieval design
After this landmark, you can name the retrieval-design decisions (chunking, indexing, freshness, ranking), that most often determine whether a grounded system actually stays grounded.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
A retrieval-augmented system is only as good as what it retrieves, and retrieval quality is set by decisions made long before the model sees anything: how documents are chunked (too large and irrelevant text dilutes the answer; too small and context is lost), how relevance is ranked (pure similarity search misses exact-match cases; keyword search misses paraphrase), and how fresh the index is (a stale index confidently returns outdated facts, indistinguishable from current ones). Teams debug retrieval problems by tuning the prompt, when the actual bug is upstream; the right document was never retrieved.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
When an AI search tool gives you an oddly incomplete or outdated-feeling answer, the model didn’t fail; the underlying search likely didn’t retrieve the right source. Try a more specific query, or check the primary source directly.
MAKE A DECISION
A company’s internal AI assistant, grounded in the employee handbook, is giving confident answers that turn out to reference a policy that was replaced two months ago. What’s the root cause to check first?
CARRY THISFor one AI search or Q&A tool you rely on, find out (or ask) how often its underlying index refreshes. Does that match how current you assumed its answers were?