Efficiency and Optimization in Data Set Transformation

One of the core values of DSPy is the ability to add “reasoning modules” such as Chain-of-Thought to your LLM programs!

For example, Chain-of-Thought describes prompting the LLM with “Let’s think step by step …”. Interestingly, this meta-prompt around asking the LLM to think this way dramatically improves performance in tasks like question answering or document summarization.

Self-Discover is a meta-prompting technique that searches for the optimal thinking primitives to integrate into your program! For example, you could “Let’s think out of the box to arrive at a creative solution” or “Please explain your answer in 4 levels of abstraction: as if you are talking to a five year old, a high school student, a college student studying Computer Science, and a software engineer with years of experience in the topic”.

I am SUPER excited to be publishing our 90th Weaviate Podcast with Chris Dossman! Chris has implemented Self-Discover in DSPy, one of the most fascinating examples so far of what the DSPy framework is capable of!

Chris is also one of the most talented entrepreneurs I have met during my time at Weaviate thanks to introductions from Bob van Luijt and Byron Voorbach. Chris built one of the earliest RAG systems for government information and is now working on LLM opportunities in marketing with his new startup, Dicer.ai!

I hope you enjoy the podcast, it was such a fun one and I learned so much!