I remember very clearly that till a few months ago, Prompt Engineering was all the hype. The entire job market was filled with the role of prompt engineers, but not so anymore. Prompt engineering was not any art or science, it was just a clever Hans phenomenon, humans putting up the necessary context for the system to answer in a better way. People even wrote books/blogs like the Top 50 prompts to get the best of GPT, so on and so forth. But large-scale experiments have clearly shown that there is no single prompt or strategy that works for all kinds of problems, it’s just some prompts appear to be better in isolation but are a hit-and-miss when analyzed comprehensively. So, today we are going to talk about DSPY: COMPILING DECLARATIVE LANGUAGE MODEL CALLS INTO SELF-IMPROVING PIPELINES, a framework developed by Stanford for self-improvement pipelines, where LLMs are treated as a module that is being optimized by a compiler, similar to the abstractions found in PyTorch.