Entity trail

MIPROv2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-07-17 / skill-finderMake your DSPy metric return text, not just a score, to unlock GEPA prompt optimizationGEPA (Genetic-Pareto) is the 2026 gold-standard DSPy optimizer: a reflective LM reads your metric's textual feedback and mutates instructions and few-shots, beating MIPROv2 on complex tasks with far fewer rollouts. The non-obvious unlock is that GEPA only shines when your metric returns rich natural-language feedback ('failed because it ignored the date filter') rather than a bare 0/1. Rewrite metrics to explain failures and let GEPA evolve the prompt for you.
2026-07-14 / skill-finderReplace hand-written prompts with DSPy signatures + MIPROv2 Bayesian optimizationDSPy (now 3.3.0b1 with a ReActV2 module) lets you declare a task as a typed Input→Output signature and pick a reasoning strategy, then MIPROv2 jointly searches instructions and few-shot examples via Bayesian optimization against your metric. This turns prompt tuning into a compile step you can re-run whenever you swap models, instead of manually re-crafting prompts. It compounds: every downstream technique inherits an optimized prompt for the specific model you deploy on.
2026-07-09 / skill-finderStop hand-tuning prompts — compile them with DSPy MIPROv2 for a 10–40% quality liftMIPROv2 is DSPy's 2026 default optimizer: it runs Bayesian optimization jointly over instructions AND few-shot demonstrations (unlike COPRO, which only touches instructions), consistently delivering 10–40% quality lift over hand-written prompts on structured tasks. Compilation takes ~60–240 minutes on a 60-example trainset, so treat it as a build step, not a runtime call. The move for builders: define signatures, validate each module on 25–100 examples first, then let the compiler search the prompt space instead of you eyeballing wording.
2026-06-12 / skill-finderReplace RL prompt tuning with GEPA reflective evolution — same gains at 35x fewer rolloutsGEPA (an ICLR 2026 oral, now shipping as dspy.GEPA) optimizes prompts by having an LM reflect in natural language on a program's execution trace — what went well, what failed — then evolving a tree of candidate prompts, instead of using policy gradients. Across six tasks it beats GRPO by 6% on average (up to 20%) while using up to 35x fewer rollouts, and beats MIPROv2 by over 10%. Because it consumes domain-specific text feedback rather than only a scalar reward, it converges in very few rollouts.

Graph relationships

USES
DSPy
DSPy uses MIPROv2 for Bayesian optimization of prompts and few-shot examples.
Source finding
SUPPORTS
DSPy
MIPROv2 is DSPy's 2026 default optimizer for prompt compilation.
Source finding
USES
DSPy -> MIPROv2
DSPy uses MIPROv2 for Bayesian optimization of prompts and few-shot examples.
Source finding
SUPPORTS
DSPy -> MIPROv2
MIPROv2 is DSPy's 2026 default optimizer for prompt compilation.
Source finding

Source trail

DSPy GEPA DSPy — GEPA Optimization CallSphere — DSPy Prompt Optimization with MIPROv2 (2026)

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