Large Language Models (LLMs) have gained significant attention in recent years, yet understanding their internal mechanisms remains challenging. When examining individual attention heads in Transformer models, researchers have identified specific functionalities in some heads, such as induction heads that predict…

Large reasoning models (LRMs) have shown impressive capabilities in mathematics, coding, and scientific reasoning. However, they face significant limitations when addressing complex information research needs when relying solely on internal knowledge. These models struggle with conducting thorough web information retrieval…

This article demonstrates how to build an intelligent routing system powered by Anthropic’s Claude models. This system improves response efficiency and quality by automatically classifying user requests and directing them to specialised handlers. The workflow analyses incoming queries, determines their…

Recent advancements in LLMs have significantly improved natural language understanding, reasoning, and generation. These models now excel at diverse tasks like mathematical problem-solving and generating contextually appropriate text. However, a persistent challenge remains: LLMs often generate hallucinations—fluent but factually incorrect…