[lordofmoria]

This is a great question!

I think we now know, collectively, a lot more about what’s annoying/hard about building LLM features than we did when LangChain was being furiously developed.

And some things we thought would be important and not-easy, turned out to be very easy: like getting GPT to give back well-formed JSON.

So I think there’s lots of room.

One thing LangChain is doing now that solves something that IS very hard/annoying is testing. I spent 30 minutes yesterday re-running a slow prompt because 1 in 5 runs would produce weird output. Each tweak to the prompt, I had to run at least 10 times to be reasonably sure it was an improvement.

[codewithcheese]

It can be faster and more effective to fallback to a smaller model (gpt3.5 or haiku), the weakness of the prompt will be more obvious on a smaller model and your iteration time will be faster

[JeremyHerrman]

great insight!

[sroussey]

How would testing work out ideally?