HedgeFair

DSPy is a SUPER exciting advancement for AI and building applications with LLMs!🧩🤯 Pioneered by frameworks such as LangChain and LlamaIndex, we can build much more powerful systems by chaining together LLM calls! This means that the output of one call to an LLM is the input to the next, and so on. We can think of chains as programs, with each LLM call analogous to a function that takes text as input and produces text as output. DSPy offers a new programming model, inspired by PyTorch, that gives you a massive amount of control over these LLM programs. Further the Signature abstraction wraps prompts and structured input / outputs to clean up LLM program codebases. 🧼 DSPy then pairs the syntax with a super novel *compiler* that jointly optimizes the instructions for each component of an LLM program, as well as sourcing examples of the task. ⚙️ Here is my review of the ideas in DSPy, covering the core concepts and walking through the introduction notebooks showing how to compile a simple retrieve-then-read RAG program, as well as a more advanced Multi-Hop RAG program where you have 2 LLM components to be optimized with the DSPy compiler! I hope you find it useful! 🎙️ youtube.com/watch?v=41EfOY0L…