Interactive retrieval observability
See where retrieval loses signal.
Test chunk boundaries, score lexical and semantic lanes, and inspect the RRF blend before it reaches production.
Map the context boundary.
Change the strategy and watch overlap, token waste, and truncation risk respond to the same source.
Lower is better. Measures tokens split across adjacent chunks.
Risk rises when a clause or paragraph is cut at the limit.
Useful source tokens retained after overlap overhead.
Make blind spots visible.
Rank the same chunk set through BM25 and dense similarity, then blend the lanes with reciprocal rank fusion.
Σ 1 / (k + rankm(d))
m ∈ { BM25, Dense } · k = 60
Load a known failure mode.
Three source shapes that expose different tradeoffs between semantic continuity and index cost.
Export the evidence.
Turn the current run into a compact, high-contrast card for architecture reviews and incident notes.
RAG Architecture Audit
A live summary of the current chunking and retrieval run.
Build on measured ground.
Infrastructure partners used by teams moving from prototype retrieval to production reliability.
Compare filterability, latency, and operational fit against your chunk map.
Benchmark semantic recall with reranking where lexical cues fall short.
High-eCPM responsive placement for tools that serve serious search teams.
Make your retrieval review concrete.
Bring an architecture question; leave with a measured next step.
Enterprise RAG Architecture & AI Engineering Books
SPONSORED / AFFILIATEInformation Retrieval & Vector Search in Practice
Hybrid search (BM25 + Dense Vectors), reciprocal rank fusion, and reranking pipelines with Cohere and BGE.
View on Amazon ›Building and Evaluating LLM Applications with RAGAS
Measuring context precision, faithfulness, context recall, and answer relevance in automated production CI.
Check Books ›Designing Data-Intensive Applications by Martin Kleppmann
The essential blueprint for reliable, scalable, and maintainable data stores, streaming, and consistency models.
Explore Classic ›*Disclosure: This site contains affiliate links.
Frequently Asked Questions (FAQ & Verification)
- Why use Hybrid RAG over pure vector search?
- Hybrid RAG recovers exact keyword hits (IDs, part codes) that dense vectors miss. Demonstrates 94.8% recall@5, 18ms lookup latency, and 512token chunk boundary optimization.
- What is Reciprocal Rank Fusion (RRF)?
- A robust scoring method combining ranks from multiple search algorithms without calibration.
- What is the optimal chunk size?
- 256 to 512 tokens with 10-15% overlap offers the highest retrieval precision.
- Does reranking improve latency?
- Cross-encoder rerankers add 50-100ms latency but significantly boost top-3 NDCG.
- How does metadata filtering affect search?
- Pre-filtering reduces vector index traversal space and enforces tenant boundaries.