The rise of LLMs has elevated prompt engineering as a critical skill in the AI industry, but manual prompt tuning is often inefficient and model-specific. This talk explores various automatic prompt optimization approaches, ranging from simple ones like bootstrapped few-shot to more complex techniques such as MIPRO and TextGrad, and showcases their practical applications through frameworks like DSPy and AdalFlow. By exploring the benefits, challenges, and trade-offs of these approaches, the attendees will be able to answer the question: is prompt engineering dead, or has it just evolved?

Explore the inner workings of deep learning frameworks like TensorFlow and PyTorch by building your own in this workshop. We will start with the fundamental automatic differentiation mechanics and proceed to implementing more complex components like layers, modules and optimizers. This workshop is mainly designed for experienced data scientists, who want to expand their intuition about lower level framework internals.