Imagine walking into the world’s largest library with a mission to answer a single question. You’re surrounded by thousands of books; some helpful, most irrelevant, and a few totally misleading. This is exactly what working with Large Language Models (LLMs) feels like: without the right context, it’s easy to get lost in the noise.
Too little context, and the model guesses. Too much, and it fixates on irrelevant details. Like a smart but over-eager assistant, an LLM needs just enough clearly framed information, to respond accurately. But how much is “just enough”? The truth is, there’s no one-size-fits-all sweet spot; every use case demands a different balance.
In this talk, I’ll focus on the art of context refinement when integrating LLMs into production and show how avoiding common, costly mistakes can make your models smarter and your applications more reliable. You’ll learn practical techniques to balance input size, design effective prompts, minimize hallucinations, and build feedback-aware systems that thrive under ambiguity.
Whether you're scaling prototypes or building robust applications, I will share actionable insights to make LLMs more reliable, efficient, and aligned, by giving them exactly what they need, and nothing more.