Royal Galician Academy
PulseAugur coverage of Royal Galician Academy — every cluster mentioning Royal Galician Academy across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
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Healthcare RAG AI fails, retrieving wrong patient data and causing $850K HIPAA fine
A healthcare AI system using Retrieval-Augmented Generation (RAG) mistakenly provided treatment recommendations for one patient to another due to similar names and medical terminology. The system, which used OpenAI's te…
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RAG chunk boundary bugs cause retriever to return incomplete sentences
This article discusses a common issue in Retrieval-Augmented Generation (RAG) systems where chunk boundaries can lead to incomplete or "half-sentence" retrievals. It explains how the way documents are split into smaller…
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Firecrawl and Crawl4AI offer new web scraping methods for RAG
The article compares two web scraping tools, Firecrawl and Crawl4AI, designed for Retrieval-Augmented Generation (RAG) pipelines. It highlights the challenge of feeding raw HTML to LLMs due to token limits, costs, and a…
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AI-native development shifts focus from coding to natural language prompts
AI-Native Development is emerging as a new paradigm where developers describe desired outcomes in natural language rather than writing explicit code. This approach leverages prompt engineering, Retrieval-Augmented Gener…
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ROZA Graphs improve RAG accuracy and efficiency via evidence-centric feedback
Researchers have developed ROZA Graphs, a novel approach to enhance Retrieval-Augmented Generation (RAG) systems by incorporating evidence-centric feedback. This method stores per-evidence chains of thought as structure…
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LoRA emerges as a viable parametric knowledge memory for LLMs, complementing RAG and ICL
A new paper explores the use of Low-Rank Adaptation (LoRA) as a method for continuously updating knowledge in large language models. The research empirically analyzes LoRA's capacity, composability, and optimization for…
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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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DoGMaTiQ pipeline automates QA nugget generation for report evaluation
Researchers have developed DoGMaTiQ, a new pipeline designed to automatically generate question-and-answer (QA) nuggets for evaluating long-form reports, particularly those generated by retrieval-augmented generation (R…
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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…