Tag: dspy
All the articles with the tag "dspy".
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GEPA: How an LLM Can Write a Better Prompt Than RL Can Train One
A walkthrough of GEPA (Agrawal et al., ICLR 2026), the reflective prompt optimiser that beats GRPO with up to 35× fewer rollouts by reading its own trace logs in plain English. The four-step loop, a worked iteration on a multi-hop QA system, the Pareto trick that keeps the candidate pool diverse, and where 98% of the rollout budget actually goes.
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Inside MIPROv2: Bootstrap, Propose, Search
A walkthrough of MIPROv2 (Opsahl-Ong et al., 2024), DSPy's flagship prompt optimiser. The three-phase pipeline (bootstrap, propose, search), how Bayesian Optimisation makes the discrete combinatorial space tractable, what changes between the baseline and the compiled prompt, and a decision rule for when to run it.
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Prompts are Hyperparameters
A practitioner's tour of DSPy, MIPROv2 and GEPA. The reframe (prompts are parameters of an LLM program, not the artefact you ship), the five axes any optimiser can tune, how MIPROv2 and GEPA actually work, where this set of methods quietly disappoints, and a decision tree for picking one.
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TextGrad: Automatic Differentiation Through LLM Critiques
A walkthrough of TextGrad (Yuksekgonul et al., Nature 2025), an autograd engine where the gradients are natural-language critiques. The PyTorch-shaped API, the four-step optimisation loop, why one framework optimises prompts, code and molecules with the same machinery, and where DSPy is the better bet.