Named Entity Recognition (NER) is a cornerstone of NLP, identifying key entities like people, places, and organizations within text. Traditional NER systems require extensive training data for each specific entity type, limiting their ability to handle new or unusual entities. Recent research explores how large language models (LLMs) can perform "zero-shot" NER, recognizing entities they haven't explicitly been trained on. However, even these powerful models often struggle with unseen entity types. A new approach called SLIMER (Show Less, Instruct More - Entity Recognition) aims to overcome this limitation by providing the LLM with more detailed instructions, including definitions and guidelines, while using significantly less training data. Imagine teaching an AI to identify "cryptocurrencies" without showing it thousands of examples. Instead of relying on brute-force memorization, SLIMER provides the model with a definition of what a cryptocurrency is and guidelines on how to distinguish it from other financial terms. This method allows the model to generalize its understanding and identify even obscure cryptocurrencies it hasn't encountered before. Tested on standard NER benchmarks like MIT, CrossNER, and BUSTER (a challenging financial NER dataset), SLIMER performs competitively with existing state-of-the-art zero-shot NER models despite training on a much smaller dataset. The key innovation lies in providing richer, more descriptive prompts that enhance the LLM’s ability to generalize its knowledge. This research suggests a promising shift in how we train LLMs for complex tasks. By moving away from data-heavy approaches and focusing on smarter instruction methods, we can unlock more efficient and adaptable AI systems. The future of NER might be less about showing and more about telling. This approach not only tackles the challenge of unseen entities but also offers potential advantages in efficiency, requiring far less data preparation and training time. While further research is needed to address scaling challenges for large numbers of entity types, SLIMER presents an exciting step towards more robust and practical zero-shot NER solutions.