Large language models (LLMs) are revolutionizing how we interact with technology, but their effectiveness often hinges on the quality of the input prompts. Ever struggled to get an LLM to understand exactly what you want? New research explores how human feedback can be the key to unlocking an LLM's full potential. Traditionally, optimizing prompts relied on numerical scores or validation sets, which aren't always practical in real-world scenarios. This research introduces a novel approach called Prompt Optimization with Human Feedback (POHF). Instead of relying on hard-to-obtain scores, POHF leverages simple preference feedback. Imagine showing an LLM two responses generated from different prompts and simply choosing which one you prefer. This iterative process, inspired by a technique called "dueling bandits," allows the model to learn and refine its understanding of your needs. The researchers developed an algorithm called Automated POHF (APOHF) that acts as an intermediary between the user and the LLM. The user provides an initial task description and then offers preference feedback on pairs of responses. APOHF uses this feedback to train a neural network that predicts the performance of different prompts, effectively learning what kind of prompts produce the desired results. The results are impressive. APOHF efficiently finds effective prompts using minimal feedback, outperforming existing methods in various tasks, including optimizing user instructions, refining text-to-image generation, and even enhancing response quality. This research opens exciting possibilities for improving human-LLM interaction. By simply expressing preferences, users can guide LLMs to generate more relevant and helpful outputs. While challenges remain, such as preventing malicious prompt engineering, POHF represents a significant step towards making LLMs more intuitive and accessible to everyone.