#retrieval
4 papers
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inspiration
Retrieval Is a Ranking Problem
Most RAG systems that disappoint are not failing at generation. They are failing at retrieval — and specifically at ranking. Swapping vector databases rarely fixes it. Two-stage retrieval and honest evaluation usually do.
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inspiration
The Embedding Is Not the Default
Every RAG system encodes text into vectors. The model that produces those vectors — and the dimensionality you accept from it — is an engineering decision. Most engineers make it once, at setup, and never revisit it. That is the wrong posture.
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whitepaper
RAG Is a Retrieval Problem: Chunking, Indexing, and Why Engineers Get It Backwards
Most RAG failures happen before the LLM sees a single token. Chunking and indexing are not preprocessing steps — they are architectural decisions that determine what the model can possibly know. This paper maps the engineering decisions that actually matter: chunk strategy, index choice, hybrid retrieval, and the failure modes that remain invisible until production.
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inspiration
Retrieval Is the Weakest Link
RAG systems fail at retrieval, not generation. Engineers blame the LLM. The problem is upstream.