The emergence of complex reasoning abilities in large language models (LLMs) has sparked great interest, and a variety of prompting techniques was proposed to coax them into emulating human thought processes. In this work, we introduce Think Node-by-Node, a graph-based reasoning framework inspired by mind maps, flowcharts, and other visual aids that help humans tackle complex problems. Rather than generating images directly, our approach leverages standard graph-building and rendering libraries, and requires no fine-tuning, only the model’s native coding capabilities. We further explore a “Fast Thinking” regime, in which a graph-reasoning example provided in the prompt, but the model generates the answers directly, without the full thought process reconstruction. Surprisingly, this approach leads to significant improvement upon baseline in general-knowledge tasks. Remarkably, Think Node-by-Node maintains strong performance even under a strict 25-token budget for answer generation. Across two instruction-tuned LLMs (0.5B and 7B parameters), our FastTNbN strategy outperforms baseline prompting techniques, improving accuracy by up to 10%, and exceeds the capabilities of other structured prompting methods under equivalent generation constraints.