RAG Retrieval Methods

Framework of four retrieval patterns for AI agents: (1) database filters for structured rows when the answer lives in a small subset, (2) SQL queries for totals, averages, rankings, and trends, (3) full-context stuffing when order and completeness matter and the document fits the context window, and (4) chunk-based vector search for needle-in-haystack semantic lookup. Chosen by asking what method a human would use on the same question. Counters the default-to-vectors habit.

Google Cloud's Vector Search 2.0 video adds a managed hybrid-search implementation: developers define fields to embed, load records with empty vector fields, and let the service generate embeddings and combine semantic and keyword results into one ranked list. SQL-like filters remain available for price, category, and other structured business rules [src-216].

Related entities

Source references

  • [src-006] Nate Herk cluster — Nate Herk — RAG and data ingestion cluster (5 videos)

– Videos referenced: kOKavHnlPik

  • [src-216] Google Cloud Tech – "Give your app search superpowers: Agent Retrieval (Vector Search 2.0)" (2026-07-16)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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