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.
Keeping Your Vector Database Fresh: Strategies for Dynamic Document Stores
Keeping your vector database fresh ensures accurate search results and a seamless AI experience. This post explores change detection and efficient updates to keep your vector embeddings synchronized with dynamic content.
LlamaIndex: Enabling Data-Augmented LLM Applications
In the ever-evolving world of artificial intelligence, integrating custom data with large language models (LLMs) has become crucial for building intelligent applications.
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