Large Language Models (LLMs) have shown remarkable abilities, from writing poems to summarizing articles. But when it comes to complex reasoning, they often fall short. Why? Because they lack the structured, conceptual knowledge humans use to navigate intricate problems. Imagine trying to solve a physics problem without understanding the underlying concepts of gravity or force—that’s the challenge LLMs face. This research paper explores how to bridge this gap by introducing two innovative prompting techniques: Conceptual In-Context Learning (C-ICL) and Chain of Concepts (CoC). These methods go beyond simply showing LLMs examples, instead providing them with the core concepts necessary for genuine problem-solving. C-ICL presents all the conceptual information at once, like a mini-lesson. CoC, on the other hand, feeds the concepts incrementally, mimicking a human learning process. The researchers tested these methods on a real-world challenge: generating proprietary data models, complex structures used in industry to organize information. These models are often specific to a company and not readily available, making their creation a difficult and time-consuming task for experts. The results? CoC and C-ICL significantly outperformed standard prompting methods. They produced more accurate and original data models, showing fewer signs of simply mimicking the provided examples, a common LLM pitfall known as 'parroting.' Moreover, the generated responses often included explanations of the reasoning process, offering a glimpse into the LLM’s enhanced understanding and enabling easier validation. While CoC generally yielded the highest quality results, C-ICL proved a faster alternative for simpler problems. This research points towards exciting new possibilities for using LLMs to tackle complex tasks in science and engineering. By equipping LLMs with the right conceptual tools, we can unlock their true potential and revolutionize problem-solving across various fields. The future looks bright as researchers further refine these techniques, exploring how to optimize for different types of problems and adapt them for even more powerful, multimodal LLMs capable of processing images, sound, and text.