GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization. Making your brand machine-readable and increasing its chances of being ...

In 2026, organizations are tackling the “semantic gap” in AI outputs by embedding LLM-as-judge evaluations, multi-prompt chains, and human oversight directly into CI/CD pipelines. Tools like Vellum, ...

AI agents have fundamentally changed the threat model of AI model-based applications. By equipping these models with plugins (also called tools), your agents no longer just generate text; they now ...

One of the key challenges of building effective AI agents is teaching them to choose between using external tools or relying on their internal knowledge. But large language models are often trained to ...

A library for building search pipelines for local LLMs that produce Perplexity-style answers, but self-hosted and without API costs or limits. Searches Bing + DuckDuckGo, filters noise before fetching ...

Abstract: This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures from the ...

As AI continues to reshape the way developers build applications, Microsoft's Semantic Kernel is emerging as a powerful tool for integrating AI-driven capabilities into existing codebases -- without ...