重要指標：失敗原因の70%は分割
RAGが正しい資料を拾わない理由｜チャンク境界断裂・表崩れ・メタデータ欠落の対策
RAGの検索失敗の7割は「PDFの表や見出しが固定長チャンクで分断されること」が原因であり、Markdown構造認識型のセマンティック分割と親文書参照で解消します。
定量比較とチェックリストを読む →Interactive retrieval observability
See where retrieval loses signal.
🎙️ 会議の議事録作成をゼロ分にする｜AIボイスレコーダー＆会議用マイクトップ3
※Amazonタイムセール・楽天ポイント還元対応
1
Comu Action Pro｜月額無料・140言語対応 AIボイスレコーダー
総合評価 9.8/10｜ランニングコスト0円で会議・面談を即座に高精度テキスト＆要約化
2
Comulytic Note Pro｜MagSafe対応 カード型AIボイスレコーダー
総合評価 9.6/10｜スマホ背面にピタッと装着・通話録音も対面会議もこれ1台
3
Anker PowerConf S500｜Zoom認証 会議室用ワイヤレススピーカーフォン
総合評価 9.3/10｜複数人の発言もクリアに集音・AI文字起こし精度をさらに引き上げる
Test chunk boundaries, score lexical and semantic lanes, and inspect the RRF blend before it reaches production.
v0.9.4NO DATA LEAVES YOUR BROWSERk = 60
RUNTIME TELEMETRYRUN_8F2A
LEXICAL LANE / BM250.74
DENSE LANE / COSINE0.88
RRF TOP-1 / BLENDED0.032
INDEX HEALTHREADY
06active chunks
1,284tokens indexed
42mssimulated p95
100%local / private
01 / CHUNKING PLAYGROUND
Map the context boundary.
Change the strategy and watch overlap, token waste, and truncation risk respond to the same source.
source_document.md0 chars
chunk_map / visual boundaryLOCAL SPEC
Paste a document to render chunk boundaries.
source span semantic split overlap zone
boundary diagnosticsderived from current map
token fragmentation8.2%
Lower is better. Measures tokens split across adjacent chunks.
boundary truncation risk12%
Risk rises when a clause or paragraph is cut at the limit.
context efficiency88%
Useful source tokens retained after overlap overhead.
02 / HYBRID FUSION ENGINE
Make blind spots visible.
Rank the same chunk set through BM25 and dense similarity, then blend the lanes with reciprocal rank fusion.
RRF_SCORE(d)
Σ 1 / (k + rankm(d))
m ∈ { BM25, Dense } · k = 60
Σ 1 / (k + rankm(d))
m ∈ { BM25, Dense } · k = 60
document / chunkBM25DenseRRF blend
■ BM25 exact terms■ dense semantic signal■ reciprocal rank fusion
03 / FIELD PRESETS
Load a known failure mode.
Three source shapes that expose different tradeoffs between semantic continuity and index cost.
04 / ARCHITECTURE AUDIT CARD
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.
chunk strategyFixed-size
chunk / overlap420 / 64 chars
token efficiency88%
hybrid top score0.0323
05 / ECOSYSTEM SIGNALS
Build on measured ground.
Infrastructure partners used by teams moving from prototype retrieval to production reliability.
Vector DB partner
Qdrant / Pinecone / MilvusCompare filterability, latency, and operational fit against your chunk map.
Embedding model API
Voyage AI / Cohere RerankBenchmark semantic recall with reranking where lexical cues fall short.
Sponsored slot
Reach RAG buildersHigh-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 / AFFILIATE
RAG & RETRIEVAL SYSTEMS
Information Retrieval & Vector Search in Practice
Hybrid search (BM25 + Dense Vectors), reciprocal rank fusion, and reranking pipelines with Cohere and BGE.
View on Amazon ›
EVALUATION FRAMEWORKS
Building and Evaluating LLM Applications with RAGAS
Measuring context precision, faithfulness, context recall, and answer relevance in automated production CI.
Check Books ›
ENTERPRISE DATA PIPELINES
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.
PR・AI業務効率化ハードウェア決定版（月額課金ゼロ）
KINGTOP AIボイスレコーダー部門 1位・2位独占モデル
会議・商談・要件定義の議事録工数を90%削減する「Comu Action Pro & Comulytic Note Pro」
毎月のサブスク課金が不要で、録音から113言語のAI文字起こし・構造化要約・マインドマップ生成までワンタップで完結。厚さ約3mmのカード型でスマホ背面に装着でき、対面会議からWeb会議まで抜け漏れゼロの記録を実現します。