rag
8 posts — newest first.
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Hybrid Search Measured: Where Fusion and Reranking Actually Help
BM25, dense, RRF and a cross-encoder reranker on SciFact, with paired confidence intervals. Fusion helped BM25 a lot and dense a little; the reranker hurt.
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Your Chunking Benchmark Is Measuring Chunk Size
14 chunking strategies on SQuAD with answer-span ground truth. Semantic chunking lost to every alternative, and fixed-k scoring hid the thing that mattered.
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Vector Index Trade-offs: Flat vs IVF vs HNSW, and When Quantization Pays
Exact search is O(n) and always right. Every ANN index buys speed with recall, memory, or build time. The arithmetic, compared.
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Your runbooks are lying to your agents: RAG for on-call knowledge
On-call agents are only as good as what they retrieve. Making runbook RAG fresh, exact-match aware, and poison-resistant — with a runnable starter repo.
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Context engineering: the window is a budget, not a bucket
The context window is your agent's working memory, not a junk drawer. Four operations — write, select, compress, isolate — and a token budget you allocate.
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What are vector embeddings?
A primer on vector embeddings — how meaning becomes something you can search, cluster, and compare, and the failure modes you only see in evaluation.
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The CAP theorem in AI-native distributed systems
CAP didn't get repealed when LLMs showed up. How the C/A/P trade-offs shift when the datastore is a vector index, context graph, or retrieval layer.
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What is Retrieval-Augmented Generation (RAG)?
A primer on Retrieval-Augmented Generation — grounding an LLM's answer in documents you trust. Indexing, serving, and the failure modes that bite.