Question

A Generative AI Engineer is building a RAG application over a company's internal knowledge base. Stakeholders require that every generated answer include a citation showing the document title, section heading, and a link back to the original source page. The engineer is designing the document preparation pipeline that produces chunks for embedding and indexing in Databricks Vector Search. What is the most effective approach to satisfy the citation requirement?

Accepted Answer

Attach metadata fields (document title, section heading, source URL) to each chunk during preparation so they are stored alongside the embeddings and returned with retrieval results.

Answer

Store only the raw chunk text in the index and instruct the LLM in the prompt to invent plausible citations for each answer.

Answer

Concatenate the document title and URL into the chunk text itself before embedding, and rely on the LLM to extract them from the retrieved passage.

Answer

Increase the chunk size so each chunk contains an entire document, guaranteeing the citation information is always present in the context.