DSPy and GEPA are framed as context engineering for searchable prompt programs
iamrobotbear · x · 2026-07-21
A thread on context engineering argues that DSPy, RLM, PEEK and GEPA all fit the same pattern: you compose, measure, evaluate, and improve small canonical calls rather than inventing bespoke sub-prompts.
- The core claim is that tasks should be decomposed so each piece stays in-distribution.
- DSPy-style signatures and optimizers such as GEPA make the prompt space searchable by reducing an infinite space of prompts into a few template shapes.
- When a composed program underperforms, each component can be evaluated against its own metric to locate the piece that is drifting out of distribution.
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