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Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG
In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution.
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Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study
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.
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InfoQ AI, ML and Data Engineering Trends Report - 2025
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.
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/articles/multimodal-rag-advanced-information-retrieval/en/smallimage/multimodal-rag-advanced-information-retrieval-thumbnail-1743669544294.jpg)
Bridging Modalities: Multimodal RAG for Advanced Information Retrieval
In this article, the authors discuss how multi-model retrieval augmented generation (RAG) techniques can enhance AI by integrating multiple modalities like text, images, and audio for deeper contextual understanding, with help of a practical example of a healthcare application.