Declarative Prompt Optimization and LLM Compilers in Production: Comparing DSPy, TextGrad, SAMMO, and AdalFlow
Production AI applications are rapidly moving away from hardcoded prompt strings and manual trial-and-error tweaking. As language model systems expand into multi-stage pipelines, retrieval-augmented generation (RAG) graphs, and multi-agent loops, manual prompt adjustments fail to scale. Changing a system prompt or upstream retrieval format frequently degrades downstream extraction, reasoning, or tool-calling performance. To resolve this fragility, the industry is adopting declarative prompt opt
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