Identifying which source documents a RAG application actually needs

Selecting the right source documents is the foundation of a RAG application's quality — retrieval can only surface knowledge that exists in the corpus, and irrelevant or low-quality sources degrade both retrieval precision and generated answer accuracy. Engineers must scope document selection to match the target use case, domain, and expected user queries before building embeddings or a vector index.

1 · Learn the must-know

• 1Source documents must directly cover the domain and question types the application is expected to answer; gaps in coverage cause the LLM to hallucinate or return 'I don't know' responses.
• 2Prefer authoritative, up-to-date, and well-structured sources (e.g., official documentation, curated knowledge bases) over noisy, redundant, or outdated content, since low-quality text propagates into retrieved context.
• 3Document format and structure matter — highly structured or semi-structured sources (tables, FAQs, structured docs) often need different parsing/chunking strategies than unstructured prose to preserve meaningful context.
• 4Redundant or near-duplicate documents can dilute retrieval quality by causing the vector search to return multiple similar-but-shallow chunks instead of diverse, relevant information.
• 5Data licensing, access permissions, and governance (e.g., Unity Catalog access controls) must be considered when selecting sources, since RAG applications should only retrieve documents the querying user is authorized to see.
• 6Iterative evaluation of retrieved chunks against sample queries is necessary to confirm the selected source set actually contains the knowledge needed, rather than assuming coverage based on document titles or metadata alone.

2 · Check your understanding