Abstract: Ever notice how your AI interactions start strong but quickly deteriorate with complexity? We've all been there – carefully crafting detailed prompts for AI models, only to receive increasingly mediocre responses as our inputs grow longer. The conventional wisdom says more context equals better results, but real-world evidence suggests otherwise. In this session, I'll share discoveries from analyzing thousands of AI interactions across various domains that reveal a surprising truth: the relationship between prompt length and response quality isn't linear – it's parabolic. There's a sweet spot, and most of us are operating well beyond it.

In this talk, we're excited to show you how we built ScaleDown, a Chrome extension that makes your AI interactions more efficient and environmentally sustainable using prompt compression! As more people use AI tools such as ChatGPT, Claude, and Gemini instead of Google Search, few realize the massive carbon footprint each interaction generates. We will talk about our journey from recognizing this hidden environmental cost to creating a solution that is helping users reduce their AI-related emissions by up to 80%. Finally, we'll share how developers can contribute to our open-source Python package powering ScaleDown's prompt compression. Whether you're interested in improving our compression algorithms, enhancing our emissions calculation methodology, or expanding compatibility with additional AI models, we'll show you how you can get involved!