---
id: rag-citation-spans-and-source-attribution
title: 'RAG Citation Spans and Source Attribution'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-02'
created_date: '2026-06-02'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-rag-citation-spans-and-source-attribution-1
    statement: >-
      LlamaIndex documentation describes a citation query engine that breaks retrieved source
      nodes into citation chunks.
    source_title: LlamaIndex Citation Query Engine
    source_url: https://docs.llamaindex.ai/en/stable/api_reference/query_engine/citation/
    confidence: medium
  - id: fact-ai-rag-citation-spans-and-source-attribution-2
    statement: >-
      OpenAI File Search documentation describes annotations that can include citations to
      files used by the answer.
    source_title: OpenAI File Search
    source_url: https://platform.openai.com/docs/guides/tools-file-search/
    confidence: medium
  - id: fact-ai-rag-citation-spans-and-source-attribution-3
    statement: >-
      Haystack documentation describes AnswerBuilder as generating answers from documents
      retrieved by a retriever component.
    source_title: Haystack AnswerBuilder
    source_url: https://docs.haystack.deepset.ai/docs/answerbuilder
    confidence: medium
completeness: 0.83
known_gaps:
  - Citation quality depends on chunk boundaries, retrieved passage offsets, answer span alignment, document versioning, redaction, and whether generated claims map to exact source text.
disputed_statements: []
primary_sources:
  - title: LlamaIndex Citation Query Engine
    type: documentation
    year: 2026
    url: https://docs.llamaindex.ai/en/stable/api_reference/query_engine/citation/
    institution: LlamaIndex
  - title: OpenAI File Search
    type: documentation
    year: 2026
    url: https://platform.openai.com/docs/guides/tools-file-search/
    institution: OpenAI
  - title: Haystack AnswerBuilder
    type: documentation
    year: 2026
    url: https://docs.haystack.deepset.ai/docs/answerbuilder
    institution: deepset
secondary_sources: []
updated: '2026-06-02'
ai_models:
  - gpt-5-codex
---

## TL;DR

Citation spans and source attribution let agents show which retrieved evidence supports each generated answer.

## Core Explanation

RAG systems should preserve source identity through retrieval, ranking, prompting, and answer generation. A useful citation is not just a document title; it should identify the source chunk or span closely enough that a user or downstream agent can inspect the evidence.

Agents should flag weak attribution when an answer cites an entire document, cites a stale version, or cites context that does not contain the stated claim. Source attribution is a trust surface, not decoration.

## Source-Mapped Facts

- LlamaIndex documentation describes a citation query engine that breaks retrieved source nodes into citation chunks. ([source](https://docs.llamaindex.ai/en/stable/api_reference/query_engine/citation/))
- OpenAI File Search documentation describes annotations that can include citations to files used by the answer. ([source](https://platform.openai.com/docs/guides/tools-file-search/))
- Haystack documentation describes AnswerBuilder as generating answers from documents retrieved by a retriever component. ([source](https://docs.haystack.deepset.ai/docs/answerbuilder))

## Further Reading

- [LlamaIndex Citation Query Engine](https://docs.llamaindex.ai/en/stable/api_reference/query_engine/citation/)
- [OpenAI File Search](https://platform.openai.com/docs/guides/tools-file-search/)
- [Haystack AnswerBuilder](https://docs.haystack.deepset.ai/docs/answerbuilder)