60 Days of Production AI Systems · chapter 4 of 10
Evidence-Driven RAG Patterns
Reranking, provenance, multi-query and graph retrieval: turning search results into evidence.
6 of 6 published
Chapter guide · 6 min readMost RAG failures are faithful answers to bad evidenceThe whole chapter in one read. Then go part by part below.- 01
Day 19 · Explainer · 2 min read
Why reranking is where retrieval becomes useful
How reranking turns broad retrieval candidates into evidence the answer can depend on.
On the map: Retrieval - 02
Day 20 · Explainer · 2 min read
Why every important AI claim needs provenance
Why important AI claims need receipts that users and teams can inspect.
On the map: Retrieval - 03
Day 21 · Explainer · 2 min read
Why RAG needs both broad context and precise evidence
Why good RAG often needs both precise snippets and enough surrounding context.
On the map: Retrieval, Vector Index - 04
Day 22 · Explainer · 2 min read
Why one user question may need many searches
Why one user question may need multiple searches before the evidence is good enough.
On the map: Retrieval - 05
Day 23 · Explainer · 2 min read
Why complex AI questions need decomposition
Why complex questions become more reliable when the system answers them in parts.
- 06
Day 24 · Explainer · 2 min read
Why some answers live in relationships, not documents
Why some answers depend on relationships that plain document search can miss.
On the map: Vector Index