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Attribution

As explained in Machine Learning Platform Engineering

Source attribution is the association of generated or retrieved information with the documents or sources that support it.

Attribution associates generated answers with the retrieved documents that support them. In RAG systems, it grounds responses in current information, reduces hallucination, and enables fact-checking. section 12.2.2

Production attribution depends on several integration choices:

  • Return structured results containing the answer, source documents, document count, and errors so interfaces can render citations and retrieval details. section 12.3
  • Number retrieved documents and assemble them with consistent Markdown formatting to clarify context for the model. section 12.3
  • Pair attribution with error handling and observability to diagnose retrieval quality, prompt effectiveness, and downstream failures. section 12.3 section 12.4.4

How Machine Learning Platform Engineering covers it

How deep it goes
Details structured metadata, document formatting, streaming events, and observability, but does not specify a citation-ranking method.
Examples
Uses numbered retrieved documents, structured source dictionaries, and streamed source events in a documentation assistant.
What it assumes
Assumes familiarity with RAG components such as embeddings, vector stores, retrievers, prompts, and language models.
What you can do after
Build RAG interfaces that display supporting documents, retrieval statistics, streamed evidence, and graceful failure states.
How it connects
Links RAG, information retrieval, embeddings, large language models, user interfaces, streaming, and observability.
Cover of Machine Learning Platform Engineering
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