Originally published on AI Tech Connect.
What you need to know DSPy makes prompting a compilation problem. You declare what you want as typed Signatures and compose Modules; DSPy's optimisers figure out the how — the exact instructions and few-shot examples — by searching against your data and a metric. The slogan is "programming, not prompting". Optimisers do the tuning you would have done by hand. BootstrapFewShot bootstraps demonstrations from your own data; MIPROv2 jointly optimises instructions and demonstrations. You hand them a training set and a metric and they search. The gains are real but task-specific. Reported improvements over manual prompting sit around 10–40%, but those are benchmark-dependent and must be measured on your eval set. The dependable win is reproducibility and painless model-swapping. It pairs with…
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