Vector LLM Architecture: Build RAG Around Evidence
Design retrieval-augmented generation around traceable evidence, controlled context, access boundaries, and separate retrieval and answer evaluations.
VECTORTOKEN LAB / CATEGORY
LLM Architecture
Give generated answers an inspectable evidence path.
Retrieval-augmented generation connects a source collection, a retriever, context assembly, and a language model. This collection approaches that architecture from the evidence outward. It does not treat a vector database as a guarantee that an answer is correct, current, or available to the requesting user.
Begin with the vector LLM architecture guide. Sketch the boundaries between trusted request context, eligible passages, candidate retrieval, context selection, and generation. Attach a diagnostic record to each boundary so that a failed answer can be traced upstream. Read the related search and governance guides when the problem involves missing passages, access changes, or stale cached responses rather than the wording of the generation prompt.
A useful review includes answerable questions, unsupported questions, conflicting revisions, and text that tries to issue instructions from inside a retrieved document. Inspect which passages reached the model and whether the final claims follow from them. Keep retrieval quality and answer support visible as separate acceptance checks.