Imagine teaching a computer to identify different Pokémon-like creatures, not by complex code, but by human-readable rules. This is the challenge of interpretable learning – creating AI that not only predicts accurately but also explains its reasoning in a way we can understand. Traditionally, AI models have struggled with this. Neural networks excel at complex tasks but work like black boxes, their decision-making processes opaque to us. Symbolic AI, using clear rules, is easier to understand but less powerful. Recent research explores a fascinating solution: Large Language Models (LLMs) combined with symbolic programming. Think of it as giving LLMs a toolbox of interpretable building blocks. Instead of a single, complex prompt, these LLMs use a series of simpler prompts, organized like a decision tree. Each prompt represents a step in the decision-making process. For instance, to identify a creature, one prompt might check for the presence of wings, another the color of its fur, and so on. These LLM-Symbolic Programs (LSPs) are not only more transparent but also more effective. Experiments show they surpass traditional AI methods in accuracy on image and text classification tasks, particularly on nuanced distinctions like identifying specific bird species from textual descriptions or classifying new Pokémon-like creatures. The key lies in the incremental learning process. LLMs are trained to summarize patterns in subsets of data, generating simple rules at each node of the decision tree. This divide-and-conquer approach simplifies the learning process and makes it easier for the LLM to extract meaningful rules. While the theoretical complexity of these programs could be high, in reality, the learned programs are surprisingly simple and efficient. The decisions are made in a few clear steps, resembling the intuitive process of human deduction. The implications are far-reaching. Interpretable AI could revolutionize fields like healthcare, enabling doctors to understand how an AI diagnoses diseases, or finance, allowing analysts to dissect AI-driven investment strategies. This research marks an important step toward bridging the gap between powerful AI and human understanding, opening a path toward a future where AI not only makes decisions but also educates us on its rationale.