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.

Related entities

Source references

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

– Videos referenced: kOKavHnlPik

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.

Recommended next

Keep reading from this thread

From 491 indexed pages and articles.

  1. Wiki concept Pinecone A managed vector database used in Claude Code RAG workflows and Jack Roberts's AI memory-system pattern. Related by retrieval
  2. Wiki concept LLM Wiki vs Semantic RAG A comparison framework for choosing between two knowledge-base architectures: the Karpathy LLM Wiki Pattern (markdown + index + LLM reader) and Related by retrieval
  3. Insight Recommendation Systems in Production How recommendation systems become production decisioning systems through signals, ranking, constraints, feedback loops, and experimentation Readers have engaged with this next