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DSPy

Articles

  • 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

    1 min

  • Automated Prompt Optimization in Production: Signatures, Teleprompters, and Metric-Driven Compilation with DSPy

    Manual prompt engineering remains one of the largest sources of technical debt in modern LLM applications. Teams routinely spend weeks hand-crafting multi-paragraph system prompts, hardcoding few-shot examples, and tweaking phrasing to extract reliable outputs from specific model checkpoints. When the underlying model is upgraded, migrated to an open-weight alternative, or integrated into a multi-step pipeline, these hand-crafted strings break, requiring another cycle of trial-and-error adjustme

    1 min