Here is a simple example of dspy:

    classify = dspy.Predict(f"text -> label:Literal{CLASSES}")

    optimized = dspy.BootstrapFewShot(metric=(lambda x, y: x.label == y.label))
                    .compile(classify,  trainset=load_dataset('Banking77'))

    label = optimized(text="What does a pending cash withdrawal mean?").label

What this does is optimize a prompt given a dataset (here Banking77).

The optimizer, BootstrapFewShot, simply selects a bunch of random subsets from the training set, and measures which gives the best performance on the rest of the dataset when used as few-shot examples.

There are also more fancy optimizers, including ones that first optimize the prompt, and then use the improved model as a teacher to optimize the weights. This has the advantage that you don't need to pay for a super long prompt on every inference call.

dspy has more cool features, such as the ability to train a large composite LLM program "end to end", similar to backprop.

The main advantage, imo, is just not having "stale" prompts everywhere in your code base. You might have written some neat few-shot examples for the middle layers of your pipeline, but then you change something at the start, and you have to manually rewrite all the examples for every other module. With dspy you just keep your training datasets around, and the rest is automated.

(Note, the example above is taken from the new website: https://dspy.ai/#__tabbed_3_3 and simplified a bit)