Concepts & principles / practice

Retrieval-augmented generation

Supply retrieved source material as context for generation, then evaluate the result against that evidence.

Canonical graph noderetrieval-augmented-generation

Grounding is a process

Retrieve relevant material, select a bounded context, generate an answer, and verify important claims against the sources. A graph can help select a relevant neighborhood. It does not eliminate the need to evaluate retrieval and generation.

Avoid borrowed certainty

A retrieved source may be stale, mistaken, or adversarial. Presence in context does not prove a generated statement follows from it. Track source relevance, unsupported claims, privacy boundaries, and prompt-injection risks.

This site exports public graph data that could support a future retrieval system. It does not currently operate a vector database or hosted answering service.

Research source

Lewis et al.: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Part of a connected world

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Outgoing connections

usesKnowledge graph
supportsAI Assistant

Connected from

supportsThe developer’s field references

Node data identity, meaning & relationships

The complete semantic record, rendered from root node: front matter.

semantic_node:
  description: Supply retrieved source material as context for generation, then evaluate the result against that evidence.
  id: retrieval-augmented-generation
  kind: practice
  name: Retrieval-augmented generation
  relationships:
  - predicate: uses
    target: knowledge-graph
  - predicate: supports
    target: ai-assistant
  type: concept

Publishing metadata

description: Supply retrieved source material as context for generation, then evaluate the result against that evidence.
draft: false
title: Retrieval-augmented generation
url: /graph/concepts/retrieval-augmented-generation/