Libretto

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

We're back at it again at Libretto, this time tackling another tough question in the world of AI development: Do few-shot examples that excel with one LLM also shine with another? https://lnkd.in/eY7esMSk Our newest blog post dives deep into this query, bringing some unexpected results to light. If you’re curious about the intricacies of model behavior and prompt engineering, this piece is definitely for you. 👀 Highlights from our exploration: * We conducted extensive testing across several models to see if few-shot successes are transferable. * Interestingly, only two of the models showed any significant correlation in performance—even different releases of the same model generally had no correlation. * The results underscore the necessity of model-specific tuning, a task that our Libretto Experiments feature greatly simplifies. Eager to learn more about how we streamline these complex tests and perhaps want to try your hand at it? Dive into the full story on our blog, and if the spirit moves you, join our beta at getlibretto.com. Let’s push the boundaries of what AI can do together!