dspy-better-together

# DSPy BetterTogether ## Goal Sequence prompt and weight optimizers, evaluate intermediate programs, and return the best candidate. ## Prerequisites - Use DSPy `3.2.1` or later in the stable `3.2.x` series. - Assign an LM directly to every predictor with `student.set_lm(lm)`. - Keep a validation set, or allow `BetterTogether` to hold out part of the trainset. - Confirm the LM provider supports fine-tuning before including `BootstrapFinetune`. ## Basic Pattern ```python import dspy lm = dspy.LM("openai/gpt-4o-mini") dspy.configure(lm=lm) student = dspy.ChainOfThought("question -> answer") student.set_lm(lm) def metric(example, pred, trace=None): return float(example.answer.lower() == pred.answer.lower()) optimizer = dspy.BetterTogether( metric=metric, p=dspy.GEPA( metric=lambda gold, pred, trace=None, pred_name=None, pred_trace=None: dspy.Prediction(score=metric(gold, pred), feedback="Check answer correctness."), reflection_lm=dspy.LM("openai/gpt-4o"), auto="light", ), w=dspy.BootstrapFinetune(metric=metric), ) compiled = optimizer.compile( student, trainset=trainset, valset=valset, strategy="p -> w -> p", ) ``` ## Strategy Choices | Strategy | Use it when | |----------|-------------| | `"p -> w"` | Start with a simple prompt-then-weight pass | | `"p -> w -> p"` | Re-optimize prompts after fine-tuning | | `"w -> p"` | Fine-tuning data is already strong | | Custom chains | Comparing prompt opti...