Search Results: DSPy

A deep dive into DSPy’s GEPA reveals that its per‑example Pareto frontiers, weighted parent selection, and mini‑batch reflection loop are the real drivers of performance – not just the surface API. By dissecting the source and experimenting with valset composition, merge settings, and proposer prompts, the author uncovers practical guidelines that can turn an underwhelming run into a powerful, exploration‑driven optimization.

Developers keep yelling at models in all caps, but the real problem usually isn’t the prompt—it’s the weights. As LLMs absorb more conventions for tone, safety, tools, and formats, “fighting the weights” has become one of the most important (and least understood) failure modes in modern AI systems.

AI agents often excel at routing requests but fail to deliver appropriate behavior in each branch. Discover how differentiable programming in DSPy transforms subjective 'good service' into measurable metrics, enabling joint optimization of entire agent workflows for consistent, adaptive behavior.

Researchers unveil Promptomatix, an open-source framework that automatically generates optimized prompts for large language models. By analyzing user intent and employing cost-aware refinement, it outperforms manual methods while reducing computational overhead, making advanced LLM capabilities accessible to non-experts.