Mehmet EmrahKonyaTR
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Retrieval-Augmented Generation (RAG)

Bilgi getirim destekli üretim (RAG)

D3 · Prompts, context, RAG, tools and agents

Retrieval-augmented generation is a generation approach that combines a model’s parametric knowledge with retrieved external information.

Review status: 2026-11-26

Technical explanation

A RAG application can retrieve documents or other resources at runtime and add selected material to the model context before it generates a response.

Conceptual boundaries

RAG is not retrieval alone, does not necessarily use vector search, and does not guarantee grounding, provenance, or factual correctness.

Provider-neutral example

A policy assistant finds the current leave policy before drafting an answer about vacation days.

Limitations

Retrieved material can be untrusted or wrong, and the original RAG study reports comparative results rather than a guarantee that every RAG answer is factual.

Related concepts

Atomic claims and evidence

  1. 1.1Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
    Source
    Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
    Source role
    Authoritative source
    Exact locator
    Abstract; Section 1: parametric/non-parametric memory and dense Wikipedia index
    Supported claim
    The original RAG paper defines RAG models as combining pre-trained parametric and non-parametric memory for language generation, with a neural retriever accessing an external index in its proposed formulation.
    Last verification
    Review due
    Scope limitation
    The neural-retriever and dense-index details describe the paper’s formulation and do not prescribe every later RAG implementation.
  2. 2.1NIST AI 100-2e2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
    Source
    NIST AI 100-2e2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
    Source role
    Authoritative source
    Exact locator
    Section 3.1.1 printed p. 39; Figure 6 printed p. 39
    Supported claim
    NIST describes RAG application context as query-dependent runtime material from external sources and recommends reviewing source and citation claims in GAI outputs.
    Last verification
    Review due
    Scope limitation
    This is risk-management guidance and does not assert that every RAG system uses the same pipeline or produces incorrect output.
    2.2NIST AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — D3 evidence slice
    Source
    NIST AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — D3 evidence slice
    Source role
    Authoritative source
    Exact locator
    MEASURE 2.5, MS-2.5-003 printed p. 32: review and verify sources and citations in GAI outputs
    Supported claim
    NIST describes RAG application context as query-dependent runtime material from external sources and recommends reviewing source and citation claims in GAI outputs.
    Last verification
    Review due
    Scope limitation
    This is risk-management guidance and does not assert that every RAG system uses the same pipeline or produces incorrect output.