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Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery

Traditional RAG systems struggle with the modality gap, leading to incomplete reasoning and hallucinations in data retrieval.

Data science

fromInfoQ

1 month ago

QCon London 2026: Reliable Retrieval for Production AI Systems

Production RAG system failures primarily stem from indexing and retrieval challenges rather than language model limitations, requiring careful document parsing, chunking strategies, and enhanced retrieval methods.

fromThe IP Law Blog

4 months ago

The Briefing - New York Times v. Perplexity AI: Copyright, Hallucinations, and Trademark Risk

In this episode of The Briefing, Weintraub Tobin partners Scott Hervey and Matt Sugarman break down The New York Times v. Perplexity AI, a lawsuit that goes beyond copyright and into largely untested trademark territory. They discuss the Times' allegations that Perplexity copied its journalism at both the input and output stages and, more significantly, that the AI attributed fabricated or inaccurate content to the Times using its trademarks.

Intellectual property law

fromTechzine Global

10 months ago

Kioxia's RAG software balances AI's SSD speed and accuracy

Kioxia's AiSAQ software update enables organizations to balance search performance and vector storage, enhancing the flexibility of their Retrieval-Augmented Generation (RAG) systems.

Artificial intelligence

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#rag-systems#rag-systems

Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery

QCon London 2026: Reliable Retrieval for Production AI Systems

The Briefing - New York Times v. Perplexity AI: Copyright, Hallucinations, and Trademark Risk

Kioxia's RAG software balances AI's SSD speed and accuracy

#rag-systems
#rag-systems