Royal Galician Academy
PulseAugur coverage of Royal Galician Academy — every cluster mentioning Royal Galician Academy across labs, papers, and developer communities, ranked by signal.
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RAG systems need advanced evaluation beyond recall to ensure faithfulness and coverage
This article series explores diagnosing issues in Retrieval-Augmented Generation (RAG) systems, moving beyond intuitive tuning to data-driven root cause analysis. It introduces a decision tree using RAGAS metrics like c…
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Developers build local LLM Wiki in C# with Ollama, Kimi as RAG alternative
This tutorial guides developers in building a local LLM Wiki using C#, Ollama, and the Kimi model. It contrasts this approach with Retrieval-Augmented Generation (RAG), suggesting the wiki method is simpler for small, s…
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AutoRAGTuner framework automates RAG pipeline optimization and reduces code churn
Researchers have developed AutoRAGTuner, a new framework designed to automate the optimization of Retrieval-Augmented Generation (RAG) pipelines. This declarative system simplifies the construction, execution, evaluatio…
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Retrieval-Augmented LLMs Enhance Cybersecurity Incident Analysis Efficiency
Researchers have developed a Retrieval-Augmented Generation (RAG) system to automate the analysis of cybersecurity incidents. This system uses targeted queries and a library of MITRE ATT&CK techniques to extract indicat…
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New E-MIA attack probes RAG systems for sensitive data via exam-style queries
Researchers have developed E-MIA, a novel method for conducting membership inference attacks against Retrieval-Augmented Generation (RAG) systems. This technique converts verifiable evidence from a target document into …
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GraphRAG enhances LLMs with knowledge graphs for deeper understanding and fewer hallucinations
GraphRAG, a new approach to Retrieval Augmented Generation (RAG), enhances Large Language Models (LLMs) by integrating knowledge graphs. This method allows LLMs to understand relationships between entities, moving beyon…
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Agentic RAG enhances LLM retrieval for complex enterprise queries
Agentic Retrieval-Augmented Generation (RAG) enhances traditional RAG systems by giving large language models more control over the retrieval process. Instead of a single retrieval step, agentic RAG involves a planning …
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Agentic RAG enhances LLM retrieval for complex enterprise queries
Agentic Retrieval-Augmented Generation (RAG) enhances traditional RAG systems by giving LLMs more control over the retrieval process. Instead of a single retrieval step, agentic RAG involves a loop of understanding, pla…
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IBM details encryption methods protecting AI RAG workflows
IBM's Alex Soto has published a blog post detailing how approximate distance preserving encryption (ADCPE) can secure data within Retrieval-Augmented Generation (RAG) systems and AI applications. The post explains the m…
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RAG evaluation systems measure retrieval, grounding, and answer faithfulness
Retrieval-Augmented Generation (RAG) systems, while popular for reducing hallucinations, require robust evaluation beyond simple retrieval metrics. These systems involve two coupled components: a retriever and a generat…
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Free tool converts websites to Markdown for LLM and RAG pipelines
A developer has created a free tool to convert website content into Markdown, which is essential for preparing data for LLM and RAG pipelines. This tool, running on Apify, automatically extracts clean Markdown, preservi…
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TERSE Tool Catalog cuts AI agent token usage by 66.6%
A new specification called TERSE Tool Catalog (TTC) has been introduced to significantly reduce the token usage for AI agent tool catalogs. Current Model Context Protocol (MCP) JSON Schema definitions are verbose and co…
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ML interview prep leads to understanding of Retrieval-Augmented Generation
The author explains Retrieval-Augmented Generation (RAG) by drawing an analogy to recommendation systems. They describe how recommendation systems learn user preferences and suggest relevant items, similar to how RAG re…
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Databricks Vector Search: Optimize embeddings, control results, and use reranking for RAG
This article outlines best practices for optimizing vector search within Retrieval-Augmented Generation (RAG) pipelines, particularly on Databricks Mosaic AI Vector Search. It emphasizes minimizing embedding dimensional…
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Databricks RAG pipeline adds content staleness tracking for fresher results
Retrieval-Augmented Generation (RAG) systems often fail to distinguish between new and old information, leading users to receive outdated content. This article proposes a solution by integrating staleness tracking and r…
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RAG+prompt system boosts Japanese-Chinese translation accuracy with linguistic analysis
Researchers have developed a retrieval-augmented generation (RAG) system combined with prompting techniques to improve Japanese-Chinese machine translation, particularly for sentences with noun-modifying clause construc…
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TagRAG framework improves knowledge graph retrieval for language models
Researchers have developed TagRAG, a novel framework for retrieval-augmented generation (RAG) that utilizes hierarchical knowledge graphs guided by object tags. This approach aims to improve upon existing RAG methods by…
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AI assistant digitizes lab know-how to improve safety and reduce errors
Researchers have developed an AI assistant designed to bridge the gap between formal laboratory documentation and practical, safe execution of experiments. This system uses first-person video and multimodal AI to extrac…
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New OCR benchmark reveals accuracy doesn't guarantee RAG performance
A new benchmark has been developed to evaluate the robustness of Optical Character Recognition (OCR) systems specifically for Retrieval-Augmented Generation (RAG) applications. Current OCR benchmarks using character-lev…
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ARGUS system uses adversarial umpiring for policy-adaptive ad governance
Researchers have developed ARGUS, a novel system designed to adapt online advertising governance to evolving regulatory policies. The system employs a three-stage framework that includes policy seeding, adversarial labe…