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DSPy – Programming–not prompting–LMs

dspy.ai

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Re: DSPy – Programming–not prompting–LMs

#2

Post by byefruit »

I've seen a couple of talks on DSPy and tried to use it for one of my projects but the structure always feels somewhat strained. It seems to be suited for tasks that are primarily show, don't tell but what do you do when you have significant prior instruction you want to tell?

e.g Tests I want applied to anything retrieved from the database. What I'd like is to optimise the prompt around those (or maybe even the tests themselves) but I can't seem to express that in DSPy signatures.

Re: DSPy – Programming–not prompting–LMs

#4

Post by behnamoh »

Every time I've seen a dspy article, I end up thinking: ok, but what does it do exactly?

I've been using guidance, outlines, GBF grammars, etc. What advantage does dspy have over those alternatives?

I've learnt that the best package to use LLMs is just Python. These "LLM packages" just make it harder to do customizations as they all make opinionated assumptions and decisions.

Re: DSPy – Programming–not prompting–LMs

#5

Post by dr_kiszonka »

behnamoh wrote: post #4

Every time I've seen a dspy article, I end up thinking: ok, but what does it do exactly? I've been using guidance, outlines, GBF grammars, etc. What advantage does dspy have over those alternatives? I've learnt that the best package to use LLMs is just Python. These "LLM packages" just make it harder to do customizations as they all make opinionated assumptions and decisions.

Question from a casual AI user, if you have a minute. It seems to me that I could get much more productive by making my own personal AI "system". For example, write a simple pipeline where Claude would scrutinize OpenAI's answers and vice versa.

Are there any beginner-friendly Python packages that you would recommend to facilitate fast experimentation with such ideas?

Re: DSPy – Programming–not prompting–LMs

#6

Post by gradys »

dr_kiszonka wrote: post #5
behnamoh wrote: post #4

Every time I've seen a dspy article, I end up thinking: ok, but what does it do exactly? I've been using guidance, outlines, GBF grammars, etc. What advantage does dspy have over those alternatives? I've learnt that the best package to use LLMs is just Python. These "LLM packages" just make it harder to do customizations as they all make opinionated assumptions and decisions.

Question from a casual AI user, if you have a minute. It seems to me that I could get much more productive by making my own personal AI "system". For example, write a simple pipeline where Claude would scrutinize OpenAI's answers and vice versa. Are there any beginner-friendly Python packages that you would recommend to facilitate fast experimentation with such ideas?

I assume you know how to program in Python? I would start with just the client libraries of the model providers you want to use. LLMs are conceptually simple when treated as black boxes. String in, string out. You don't necessarily need a framework.

Re: DSPy – Programming–not prompting–LMs

#7

Post by monkmartinez »

dr_kiszonka wrote: post #5
behnamoh wrote: post #4

Every time I've seen a dspy article, I end up thinking: ok, but what does it do exactly? I've been using guidance, outlines, GBF grammars, etc. What advantage does dspy have over those alternatives? I've learnt that the best package to use LLMs is just Python. These "LLM packages" just make it harder to do customizations as they all make opinionated assumptions and decisions.

Question from a casual AI user, if you have a minute. It seems to me that I could get much more productive by making my own personal AI "system". For example, write a simple pipeline where Claude would scrutinize OpenAI's answers and vice versa. Are there any beginner-friendly Python packages that you would recommend to facilitate fast experimentation with such ideas?

Not the person you asked, but I will take a shot at your question. The best python package for getting what you want out of these systems is to use original gangster python with libraries to help with your goals.

For your example; Write a python script with requests that hits the OpenAI API. You can even hardcode the API key because its just a script on your computer! Now you have the GPT-4proLight-mini-deluxe response in JSON. You can pipe that into a bazzillion and one different places including another API request to Anthropic. Once that returns, you can now have TWO llm responses to analyze.

I tried haystack, langchain, txtai, langroid, CrewAI, Autogen, and more that I am forgetting. One day while I was reading r/Localllama someone wrote; "All these packages are TRASH, just write python!"... Lightbulb moment for me. Duh! Now I don't need to learn a massive framework to only use 1/363802983th of it while cursing that I can't figure out how to make it do what I want it to do.

Just write python. I tell you that has been massive for my usage of these LLM's outside of the chat interfaces like LibreChat and OpenWebUI. You can even have claude or deepseek write the script for you. That often gets me within striking distance of what I really want to achieve at that moment.

Re: DSPy – Programming–not prompting–LMs

#8

Post by digdugdirk »

dr_kiszonka wrote: post #5
behnamoh wrote: post #4

Every time I've seen a dspy article, I end up thinking: ok, but what does it do exactly? I've been using guidance, outlines, GBF grammars, etc. What advantage does dspy have over those alternatives? I've learnt that the best package to use LLMs is just Python. These "LLM packages" just make it harder to do customizations as they all make opinionated assumptions and decisions.

Question from a casual AI user, if you have a minute. It seems to me that I could get much more productive by making my own personal AI "system". For example, write a simple pipeline where Claude would scrutinize OpenAI's answers and vice versa. Are there any beginner-friendly Python packages that you would recommend to facilitate fast experimentation with such ideas?

I've had good luck with a light "shim layer" library that handles the actual interfacing with the api and implements the plumbing on any fun new features that get introduced.

I've settled on the Mirascope library (https://mirascope.com/), which suits my use cases and lets me implement structured inputs/outputs via pydantic models, which is nice. I really like using it, and the team behind it is really responsive and helpful.

That being said, Pydantic just released an AI library of their own (https://ai.pydantic.dev/) that I haven't checked out, but I'd love to hear from someone who has! Given their track record, it's certainly worth keeping an eye on.

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