Check out our latest post about how many few-shot examples is optimal for your prompts!
Are Your LLM Prompts Underperforming? It Might Be Your Few-Shot Strategy Are you struggling with the performance of your AI prompts? It might not be a quality issue but rather how you're using few-shot examples. Our latest post at Libretto delves into the optimal number of few-shot examples to use, and the results are just as confusing as prompt engineering in general. https://lnkd.in/dDkwMQPK It turns out, the number of few-shot examples you use is a delicate balance. Too few and your prompts won't be as accurate as they could be; too many and you're not just wasting resources—you could actually degrade your prompt's performance. This is not just about loading in more good examples to your prompt; it's about striking the right balance to maximize efficiency and effectiveness. Our experiment also reveals a crucial lesson: the effectiveness of few-shot examples varies significantly depending on the context and the model. We tried three different models with an example prompt and got three different behaviors. There’s no one-size-fits-all answer here. At Libretto, we're learning over and over that empirical testing is the only way to know if your LLM prompts work. There are no universal truths in prompt engineering—success comes from rigorous, model-specific testing. If you're keen on enhancing your AI's performance with precise, empirically tested prompt strategies, join us at Libretto. We provide the tools to automate and refine your prompt engineering processes efficiently. 🚀 Sign up for Libretto’s beta and start optimizing today: https://lnkd.in/df4Jyyik