#rag
8 papers
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inspiration
Fine-Tuning Is Usually Not the First Move
Reaching for fine-tuning to fix a model is often the expensive wrong turn. Most problems that look like they need fine-tuning are really retrieval or prompting problems. Fine-tuning changes behavior and style; it is a poor and costly way to inject knowledge.
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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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inspiration
Position Is Not Neutral
A model that fits 128K tokens can still fail to use information you placed at token 60K. The context window is a capacity claim. Where you put information inside that window is a separate engineering decision — one with a measurable performance cost if you get it wrong.
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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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whitepaper
Fine-Tuning, RAG, or Prompting: An Engineering Decision
Three techniques can improve LLM output quality: prompt engineering, retrieval-augmented generation, and fine-tuning. Each solves a different problem. Choosing the wrong one wastes months and produces worse results than the right one done simply.
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inspiration
Embeddings Are Not Optional
Every RAG pipeline, semantic search index, and similarity feature runs on embeddings. The generation model gets the credit. The embedding model does the work.
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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.