Abstract: Ever notice how your AI interactions start strong but quickly deteriorate with complexity? We've all been there โ 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 โ it's parabolic. There's a sweet spot, and most of us are operating well beyond it.
talk-data.com
A
Speaker
Archana Vaidheeswaran
1
talks
Developer Advocate
Aleph Alpha
Archana is a Developer's Advocate at Aleph Alpha. Prior to this, she worked as a Program Manager and Community Leader in various AI Safety and women in tech communities. She has over 8 years of experience as an AI engineer. As a Board Director at Women in Machine Learning and a fellow at Python Software Foundation, she champions responsible AI development through community-driven initiatives.
Bio from: PyData Berlin 2025 August Meetup
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