Thanks! Lots to discuss from your excellent response, but I'll address the easy part first: DSPy is v2 of DSP (demonstrate-search-predict).
The DSPy paper hasn't been released yet. DSPy is a completely different thing from DSP. It's a superset. (We actually implemented DSPy _using_ DSPv1. Talk about bootstrapping!)
Reading the DSPv1 paper is still useful to understand the history of these ideas, but it's not a complete picture. DSPy is meant to be much cleaner and more automatic.
@wokwokwok Okay now we disagree.
This task is not easy, it's just easy to follow in one notebook. (If it were easy, the RAG score wouldn't be 26%.)
As for "carefully crafted string templates", I'm not sure what your argument here is. Are you saying you could have spent a few hours of trial and error writing 3 long prompts in a pipeline, until you matched what the machine does in 60 seconds?
You give DSPy (1) your free-form code with declarative calls to LMs, (2) a few inputs [labels optional], and (3) some validation metric [e.g., sanity checks].
It simulates your code on the inputs. When there's an LM call, it will make one or more simple zero-shot calls that respect your declarative signature. Think of this like a more general form of "function calling" if you will. It's just trying out things to see what passes your validation logic, but it's a highly-constrained search process.
The constraints enforced by the signature (per LM call) and the validation metric allow the compiler [with some metaprogramming tricks] to gather "good" and "bad" examples of execution for every step in which your code calls an LM. Even if you have no labels for it, because you're just exploring different pipelines. (Who has time to label each step?)
For now, we throw away the bad examples. The good examples become potential demonstrations. The compiler can now do an optimization process to find the best combination of these automatically bootstrapped demonstrations in the prompts. Maybe the best on average, maybe (in principle) the predicted best for a specific input. There's no magic here, it's just optimizing your metric.
The same bootstrapping logic lends itself (with more internal metaprogramming tricks, which you don't need to worry about) to finetuning models for your LM calls, instead of prompting.
In practice, this works really well because even tiny LMs can do powerful things when they see a few well-selected examples.
1] when prototyping, it's useful to not have to tweak each prompt by hand as long as you can inspect them easily
2] when the system design is "final", it's important to be able to tweak any prompts or finetunes with full flexibility
But we may or may not agree on:
3] automatic optimization can basically make #2 above only very rarely needed
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Anyway, the entire DSPy project has zero hard-coded prompts for tasks. It's all bootstrapped and validated for your logic. In case you're worried that we're doing some opinionated prompting on your behalf.
"A neural network layer is just a matrix. Why abstract that matrix and learn it?" Well, because it's not your job to figure out how to hardcode delicate string or floats that work well for a given architecture & backend.
We want developers to iterate quickly on system designs: How should we break down the task? Where do we call LMs? What should they do?
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If you can guess the right prompts right away for each LLM, tweak them well for any complex pipeline, and rarely have to change the pipeline (and hence all prompts in it), then you probably won't need this.
That said, it turns out that (a) prompts that work well are very specific to particular LMs, large & especially small ones, (b) prompts that work well change significantly when you tweak your pipeline or your data, and (c) prompts that work well may be long and time-consuming to find.
Oh, and often the prompt that works well changes for different inputs. Thinking in terms of strings is a glaring anti-pattern.
DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax and an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.