Dspy Better Together Agent Skill

by OmidZamani

Use for BetterTogether, prompt plus weight optimization, fine-tuning sequences, and strategy chains like p -> w -> p.

975 tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

119

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OmidZamani/dspy-skills --skill dspy-better-together

What comes with it

879 bytes besides the instruction

example.py

The instruction itself

9 sections, as written by the author

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

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 optimizers or conducting controlled experiments |

Optimizer names come from constructor keyword arguments. For example, mipro=... and gepa=... make "mipro -> gepa" valid.

Per-Optimizer Compile Arguments

Pass optimizer-specific arguments through optimizer_compile_args:

compiled = optimizer.compile(
    student,
    trainset=trainset,
    valset=valset,
    strategy="p -> w",
    optimizer_compile_args={
        "p": {"max_metric_calls": 150},
    },
)

Do not pass student inside optimizer_compile_args; BetterTogether manages the current program.

Inspect Results

The returned program exposes:

candidate_programs: evaluated candidates with score and strategy
flag_compilation_error_occurred: whether a step failed before completion

Pick optimizers: dspy-optimizer-selection
Fine-tune weights: dspy-finetune-bootstrap
Reflect with GEPA: dspy-gepa-reflective

Official Documentation

BetterTogether API: https://dspy.ai/api/optimizers/BetterTogether/
Optimizer guide: https://dspy.ai/learn/optimization/optimizers/

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