<img src="https://res.infoq.com/articles/reducing-false-positives-retrieval-augmented-generation/en/headerimage/reducing-false-positives-retrieval-augmented-generation-header-1762786576150.jpg"/><p>In this article, author Elakkiya Daivam discusses why Retrieval Augmented Generation (RAG) and semantic caching techniques are powerful levers for reducing false positives in AI powered applications. She shares the insights from a production-grade evaluation with 1,000 query variations tested across seven bi-encoder models.</p> <i>By Elakkiya Daivam</i>

تناقش إلاكيا ديفام فعالية تقنيات الجيل المعزز بالاسترجاع (RAG) والتخزين المؤقت الدلالي في تقليل الإيجابيات الكاذبة في تطبيقات الذكاء الاصطناعي. يقدم المقال نتائج تقييم من الدرجة الإنتاجية شملت 1000 نوع مختلف من الاستفسارات تم اختبارها عبر سبعة نماذج ثنائية التشفير، مما يبرز إمكانيات هذه الأساليب في تحسين دقة أنظمة الذكاء الاصطناعي.

Elakkiya Daivam analiza la efectividad de las técnicas de Generación Aumentada por Recuperación (RAG) y almacenamiento en caché semántico para minimizar los falsos positivos en aplicaciones de IA. El artículo presenta hallazgos de una evaluación de producción que involucra 1,000 variaciones de consultas probadas en siete modelos bi-encoder, destacando el potencial de estos métodos para mejorar la precisión de los sistemas de IA.

Elakkiya Daivam aborde l'efficacité des techniques de génération augmentée par récupération (RAG) et de mise en cache sémantique pour minimiser les faux positifs dans les applications d'IA. L'article présente les résultats d'une évaluation de production impliquant 1 000 variations de requêtes testées sur sept modèles bi-encodeurs, soulignant le potentiel de ces méthodes pour améliorer la précision des systèmes d'IA.

Elakkiya Daivam discusses the effectiveness of Retrieval Augmented Generation (RAG) and semantic caching techniques in minimizing false positives in AI applications. The article presents findings from a production-grade evaluation involving 1,000 query variations tested across seven bi-encoder models, highlighting the potential of these methods to enhance the accuracy of AI systems.

Article: Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study

arXiv:2512.13564v2 Announce Type: replace 
Abstract: Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often differ substantially in their motivations, implementations, and evaluation protocols, while the proliferation of loosely defined memory terminologies has further obscured conceptual clarity. Traditional taxonomies such as long/short-term memory have proven insufficient to capture the diversity of contemporary agent memory systems. This work aims to provide an up-to-date landscape of current agent memory research. We begin by clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering. We then examine agent memory through the unified lenses of forms, functions, and dynamics. From the perspective of forms, we identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory. From the perspective of functions, we propose a finer-grained taxonomy that distinguishes factual, experiential, and working memory. From the perspective of dynamics, we analyze how memory is formed, evolved, and retrieved over time. To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks. Beyond consolidation, we articulate a forward-looking perspective on emerging research frontiers, including memory automation, reinforcement learning integration, multimodal memory, multi-agent memory, and trustworthiness issues. We hope this survey serves not only as a reference for existing work, but also as a conceptual foundation for rethinking memory as a first-class primitive in the design of future agentic intelligence.

تسلط الأبحاث الحديثة الضوء على الدور الحاسم للذاكرة في الوكلاء المعتمدين على نماذج أساسية، مما يبرز الحاجة إلى الوضوح في المجال المتطور بسرعة لذاكرة الوكلاء. تحدد الدراسة ذاكرة الوكلاء من المفاهيم ذات الصلة مثل ذاكرة LLM والتوليد المعزز بالاسترجاع، بهدف تقديم نظرة شاملة على الأبحاث الحالية.

Investigaciones recientes destacan el papel crítico de la memoria en los agentes basados en modelos fundamentales, enfatizando la necesidad de claridad en el campo en rápida evolución de la memoria de agentes. El estudio delimita la memoria de agentes de conceptos relacionados como la memoria LLM y la generación aumentada por recuperación, con el objetivo de proporcionar una visión completa de la investigación actual.

Des recherches récentes soulignent le rôle crucial de la mémoire dans les agents basés sur des modèles fondamentaux, en mettant l'accent sur la nécessité de clarté dans le domaine en évolution rapide de la mémoire des agents. L'étude délimite la mémoire des agents des concepts connexes tels que la mémoire LLM et la génération augmentée par récupération, visant à fournir un aperçu complet des recherches actuelles.

Recent research highlights the critical role of memory in foundation model-based agents, emphasizing the need for clarity in the rapidly evolving field of agent memory. The study delineates agent memory from related concepts such as LLM memory and retrieval augmented generation, aiming to provide a comprehensive overview of current research.

Article: Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study

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