I have found working with DSPy to be a nice middle ground. The python script contains functions that call different DSPy optimizers (the optimized prompt packaged as a function; tested and iterated individually in a Jupyter notebook). Using DSPy `TypedPredictors` returns structured output that I then parse (ie. with conditions/loops) within the larger python script.
Can't say for sure yet if the project will work, but this workflow seems OK because DSPy abstracts away the CoT optimization with still giving me full control of the inputs/outputs in a python multi-agent script
DSPy looks interesting! But ironically I feel like learning a bit of what it does and implement some parts by my own. It is so fast to code something usable with Claude 3 opus.
Can't say for sure yet if the project will work, but this workflow seems OK because DSPy abstracts away the CoT optimization with still giving me full control of the inputs/outputs in a python multi-agent script