Hybrid Search: Dense Vector & Sparse BM25 Fusion
Layered retrieval architectures: overcoming vector search blind spots with sparse BM25 fusion, Reciprocal Rank Fusion (RRF), cross-encoders, and verifiable provenance.
Architectural Orientation
Part of the RAG Systems sub-track in Trust & Retrieval Engineering, Hybrid Search: Dense Vector & Sparse BM25 Fusion defines the critical patterns and verification criteria needed for production reliability.
Key Engineering Principles
Enforce strict input sanitization, JSON schema compliance, and post-generation guardrails to maintain system predictability.
Bind all prompt modifications to automated regression evaluation suites with quantitative threshold pass/fail assertions.
Stream token usage, p95 latency, model confidence scores, and hallucination indicators directly to enterprise OpenTelemetry collectors.
Evaluate optimal RRF constant k and dense/sparse pathway weights for domain query distributions.
Community Discussion & Feedback
Attributed peer feedback and official Netspective architecture notes.