All 16 articles, sorted alphabetically
Chunking Strategies Compared: Fixed, Recursive, Semantic and Late Chunking
Fixed, recursive, semantic and late chunking compared on the numbers that matter: answers split, recall@k, context tokens per query and index size.
Read article →HNSW Tuning for RAG: M, efSearch and the Recall Versus Latency Trade
How HNSW layers, M, efConstruction and efSearch set recall, latency and memory, why filters and deletes break it, and how to tune it.
Read article →Reciprocal Rank Fusion Tuning: When BM25 Rescues Dense Retrieval Misses
Why dense retrieval misses codes and rare terms, how reciprocal rank fusion with BM25 recovers them, and how to tune k, weights and list depth.
Read article →Cross-Encoder Reranking Depth: How Many Candidates Are Worth the Latency
How deep a cross-encoder should rerank: the recall ceiling, diminishing nDCG gains, per-pair GPU cost and the queueing that makes p95 explode.
Read article →Embedding Quantization: Binary and Int8 Vectors Without Losing Recall
How int8, int4 and binary embeddings cut vector index memory by 4x to 32x, why recall drops, and how calibration and rescoring win it back.
Read article →Query Rewriting and HyDE: Fixing the Question Before Retrieval Runs
Why raw user queries miss in RAG, how condensing, multi-query and HyDE fix them, and how rewrites break exact IDs, add latency and drift.
Read article →RAG Failure Triage: Separating Retrieval Misses From Generation Errors
A stage-by-stage method for tracing wrong RAG answers to coverage gaps, retrieval misses, prompt truncation or generation errors, with the arithmetic.
Read article →Permission-Aware RAG: Filtering by Access Control Without Destroying Recall
How post-filtering, filtered HNSW, pre-filter scans and query planners enforce document ACLs in RAG, why recall collapses, and how revocations leak.
Read article →Keeping RAG Indexes Fresh: Incremental Updates, Deletes and Tombstones
How RAG indexes go stale, why deletes become tombstones that slow search, and how change capture, chunk IDs and compaction keep answers current.
Read article →Lost in the Middle: Ordering and Packing Retrieved Context for Long Prompts
Why answers buried mid-prompt get ignored, how to order, dedupe and pack retrieved chunks under a token budget, and how to test position effects in production.
Read article →Multi-Hop Questions in RAG: Iterative Retrieval and Query Decomposition
Why one vector search cannot answer bridge questions, how iterative retrieval and decomposition chain hops, and the latency, cost and error compounding each adds.
Read article →Grounded Citations in RAG: Verifying Every Claim Against Its Source Passage
Why a citation marker proves nothing until checked, how claim-level entailment verification works, and how to trade unsupported claims against recall and cost.
Read article →RAG Latency Budget: Where the Milliseconds Go Between Query and Answer
Where time goes in a RAG request, why p95 is not the sum of stage medians, and how rewrite, rerank depth, prompt size and hedging move time to first token.
Read article →Data Lineage and Contracts for AI Knowledge Systems
How to give a RAG or agent knowledge base stable identities, source contracts, run-level and chunk-level lineage and an answer log, so you can trace a…
Read article →Graph RAG Architecture and When It Beats Vector Search
How a graph RAG system is built next to a vector index, which question shapes a graph answers better, how to design an evidence-carrying schema, resol…
Read article →Hybrid Search: Combining BM25, Dense Retrieval, and Reranking
A production guide to hybrid search: analyzers that preserve identifiers, filtered ANN, shared chunk ids, reciprocal rank fusion versus score blending…
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