Large language models (LLMs) excel at many tasks, but they often stumble when specialized knowledge is required. Think medical diagnoses or intricate legal arguments – areas where general knowledge just doesn't cut it. Traditionally, creating specialized LLMs involved extensive fine-tuning with domain-specific data, a costly and time-consuming process. But what if LLMs could learn and adapt on the fly, building their expertise as they go? Researchers have introduced the Way-to-Specialist (WTS) framework, a novel approach that pairs LLMs with evolving knowledge graphs. Imagine an LLM that not only accesses a knowledge graph for information but also contributes to its growth, constantly refining and expanding its domain expertise. This is the core concept behind WTS. It works through a two-part system: First, a 'DKG-Augmented LLM' retrieves relevant information from the knowledge graph, using it to provide more informed responses. Second, an 'LLM-Assisted DKG Evolution' component allows the LLM to generate new knowledge triples based on the questions it answers, adding to the knowledge graph. This creates a feedback loop: the LLM learns from the knowledge graph and, in turn, enriches the graph with new insights. Tests across diverse domains like medicine, natural science, and social science show that WTS significantly improves the accuracy of LLMs compared to traditional prompting methods. The most dramatic improvements were seen in areas requiring highly specialized knowledge, demonstrating the power of this dynamic learning approach. However, challenges remain. Balancing retrieval depth—how far the LLM delves into the knowledge graph—is crucial. Going too deep can introduce irrelevant information and slow down processing, while staying too shallow might miss crucial details. Another hurdle is ensuring the quality and relevance of the knowledge generated by the LLM. As WTS develops, addressing these challenges will be key to unlocking its full potential, paving the way for LLMs that truly become domain specialists.