RAG Architecture Patterns for Enterprise
Most RAG failures are not model failures. They come from retrieval, permissions, stale content, or a pipeline nobody can inspect when an answer goes wrong.
If you cannot debug retrieval, you do not have a reliable RAG system
When an answer is wrong, you should be able to tell which documents were eligible, which chunks were retrieved, what was filtered out, what was reranked, and what context the model finally saw.
Collapsing ingestion, retrieval, and generation into one opaque “AI search” step makes every failure look like a model problem. Keep the stages separate enough that retrieval quality can be measured on its own.
Authorization belongs in retrieval
Do not retrieve broadly and hope the model remembers who is allowed to see what. Tenant, user, and document permissions need to constrain the candidate set before sensitive text reaches the prompt.
Metadata filters are useful here, but only when the metadata is authoritative and kept in sync with the source system. Similarity is not an authorization rule.
Keep the pipeline boring enough to inspect
A strong baseline is document normalization, chunking around real semantic boundaries, useful metadata, retrieval, optional reranking, context assembly, generation, and source citations. Add more machinery only when an evaluation shows what it fixes.
Freshness and deletion deserve the same attention as recall. A RAG system that confidently cites a document the user can no longer access is not an accuracy problem; it is a systems and security problem.
const pipeline = {
ingest: ['normalize', 'chunk', 'embed', 'index'],
retrieve: ['vectorSearch', 'metadataFilter', 'rerank'],
respond: ['assembleContext', 'generateAnswer', 'attachCitations'],
};