Lewis et al.
PulseAugur coverage of Lewis et al. — every cluster mentioning Lewis et al. across labs, papers, and developer communities, ranked by signal.
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Debugging RAG: Prioritize Retrieval Before Prompt Tuning
This article provides a debugging strategy for addressing incorrect answers from Retrieval-Augmented Generation (RAG) systems. It emphasizes separating retrieval failures from generation failures by first logging the re…
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RAG enables AI to answer from company documents without retraining
Retrieval-augmented generation (RAG) is a technique that allows AI models to answer questions based on a specific set of documents without retraining the model. This process involves a search engine identifying relevant…
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RAG evaluation: Measuring retrieval and answer quality is key
Evaluating Retrieval-Augmented Generation (RAG) systems is complex, requiring assessment of both the retrieval of relevant information and the quality of the generated answer. Metrics like Hit@K, Recall@K, Precision@K, …
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AI agents can use simpler knowledge bases instead of complex RAG
An AI agent does not always require a complex Retrieval-Augmented Generation (RAG) pipeline to access organizational knowledge. For many common tasks, such as finding a policy or updating a document, a simpler approach …
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RAG systems improve with corrective retrieval and careful chunking · 4 sources tracked
Retrieval-Augmented Generation (RAG) systems can fail due to poor retrieval of relevant information, even if the generation model is sound. Techniques like Corrective RAG (CRAG) introduce an evaluation step to assess re…