LLM feedability / LLM citability

Definition

LLM feedability / LLM citability are two closely related concepts in working with large language models (LLMs). They focus on designing content so that it can be optimally processed by AI systems (feedability) while also allowing transparent and verifiable source attribution (citability).

  • LLM feedability: Suitability and quality of texts, data, or documents that are structured and formatted in a way that makes them easily readable, interpretable, and processable by LLMs without loss of information.
  • LLM citability: The ability of a source to be clearly and reliably cited by an LLM according to recognized standards, including machine-readable metadata (e.g., DOI, ISBN, URL) and standardized citation formats.

Target Groups

  • Researchers and students using AI for literature search or text generation
  • Journalists and editorial teams focusing on source-based reporting
  • Companies with knowledge bases, chatbots, or compliance requirements
  • Publishers and database providers preparing their content to be AI-friendly

Benefits / Purposes

  • Quality assurance: Lower error rates and clearer results through structured input
  • Traceability: Ability to verify statements using cited sources
  • Efficiency: Faster and more consistent processing of large datasets
  • Scientific rigor: Meeting academic standards when working with AI
  • Compliance: Fulfilling legal and regulatory requirements for source attribution

Key Components

  • Structured data: Headings, lists, tables, semantic markup
  • Standardized citation formats: APA, MLA, Chicago, with unique identifiers
  • Machine-readable formats: XML, JSON, HTML, Markdown, schema.org
  • Metadata: Authors, publication year, source, DOI/ISBN
  • Citation mechanisms in LLMs: Ability to embed references accurately in responses

Priorities

  • Clarity and precision in formulation and structure
  • Consistent application of citation and formatting standards
  • Transparency regarding the origin and currency of data
  • Machine readability and semantic markup
  • Minimization of hallucinations through verifiable sources

Trends

  • Integration with Retrieval-Augmented Generation (RAG) for targeted source inclusion
  • Combination with Explainable AI (XAI) to increase transparency
  • Development of AI-optimized publication standards for academia and media
  • Use of automated citation validation systems