How Lohith Reddy Kalluru Is Advancing Retrieval-Augmented AI Systems In Enterprise Environments

Lohith Reddy Kalluru is one of these engineers. He is a Cloud Developer III at Hewlett Packard Enterprise. He helps in ...
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Architecting data and AI systems that remain explainable, governable, and trusted at scale

In today’s enterprise environments, data no longer functions as a record of what has happened. It operates as a live s ...

The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall

Enterprise intent to adopt hybrid retrieval tripled from 10.3% to 33.3% in Q1 as first-gen RAG architecture failed at agentic ...

Retrieval-Augmented Generation Is An Engineering Problem, Not A Model Problem

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.

RAG precision tuning can quietly cut retrieval accuracy by 40%, putting agentic pipelines at risk

Fine-tuning RAG embedding models for precision triggers a retrieval accuracy tradeoff that standard benchmarks won't catch ...
Geeky Gadgets

LangExtract and RAG : Smarter Retrieval with Metadata Filtering

What if the key to unlocking smarter, faster, and more precise data retrieval lay hidden in the metadata of your documents? Imagine querying a vast repository of technical manuals, only to be ...
Geeky Gadgets

Unlock Hidden Data Insights with GraphRAG: The Future of AI Retrieval

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...
InfoQ

QCon London 2026: Reliable Retrieval for Production AI Systems

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...