Hallucinations in LLMs: Why Agentic Applications Are the Solution
Large Language Models (LLMs) have made remarkable progress, but hallucinations remain a persistent challenge. This report analyzes why the solution lies not in larger models but in agentic applications with robust verification mechanisms.
Reflection: Should Tokenizers Be Standardized?
Tokenization is the assembly language of AI—standardizing it could unlock true interoperability, efficiency, and modularity across language models.
Leveraging LLM Intelligence for Multi-Intent Queries in Semantic Kernel
Handling multi-intent queries in Semantic Kernel requires intelligent entity linking. We use prompt engineering, function choice behaviors, and contextual synthesis to improve AI accuracy without hardcoded logic.
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