Mainstream retrieval-augmented generation implementations typically index text in chunks, retrieve by similarity, and pass results to a model without distinguishing whether retrieved statements are policy, provisional guidance, outdated knowledge, or contradictions, which limits reliability in sensitive contexts.

Most implementations index text in chunks, retrieve based on similarity, and pass the result into a model

— Beyond Retrieval - Pyrana Context Engine Whitepaper.pdf

with limited understanding of whether a retrieved statement is foundational policy, provisional guidance, outdated tribal knowledge, or a direct contradiction

— Beyond Retrieval - Pyrana Context Engine Whitepaper.pdf

In low-risk use cases this may be acceptable. In regulated or operational settings, it is not.

— Beyond Retrieval - Pyrana Context Engine Whitepaper.pdf