Understand DSPy: Programming AI Pipelines

The origin and evolution of DSPy: Programming AI Pipelines introduces the idea, its link to ColBERT v2, retriever models, modular pipeline generation, descriptive programming, the evolution and the use case of DSPy (DSPy == Declarative Self-improving Language Programs, pythonically).
Q answered: Is DSPy only a Prompt Engineering optimization?
Q answered: Is DSPy expensive for my AI pipeline optimization?
Q answered: Can I substitute DSPy with a simple many-shot In-Context Learning prompt?
#airesearch

[@RentongGuo-cd2gq]

Thanks for the presentation! it helps a lot for the milvus community

[@JosepOriol24]

Top tier high-level presentation of the amazing DSPy vs ICL, this will help me a lot in my current line of work, keep it up!

[@densonsmith2]

You continue to stay one step ahead of me. Thank you so much!

[@StoianAtanasov]

Thanks for the presentation! Any plans to make a demo?

[@amirhosseinteymoori6537]

Nice! please explain about ReAct and also having different modules. Like ReAct and ChainOfThoughts together,

[@ruslan.vasylev]

Good info, but a bit too much slide-reading and not enough graphs or going over the code. I've been meaning to replace pure RAG in my pipeline with DSPy, but I haven't found any examples of how to actually do this.... and I'm just a bit afraid of touching this can of worms just yet before seeing someone else do the same. :D

[@DannyGerst]

What about release some code with your videos as well?

[@code4AI]

Sure. can you be more specific? What have you been not able to discover?

[@DannyGerst]

@@code4AI How you applied DSPY to optimize the prompt for the sample you meantioned in your video? Still struggle to find the first step into that. ;-)