RAG Architecture Patterns for Enterprise

Vector search limitations

Pure semantic search returns stale or irrelevant results when documents have similar embeddings.

Enterprise data requires multi-signal ranking: relevance, freshness, metadata, user context.

Hybrid retrieval

Combine vector search with BM25, metadata filtering, and temporal decay.

Rank candidates using learnable scoring functions calibrated on user feedback.

Implementation

Use Postgres with pgvector for vectors and native full-text search.

Implement LLM-as-judge re-ranking for top-k results.

Track retrieval metrics: precision@k, latency, user satisfaction.