#DSPy
3 related articles
·2 min
Stop Guessing with Your Agent: Building Measurable LLM Apps with DSPy + MLflow
Stop improving AI Agents by gut feeling. This workshop breakdown covers DSPy structured prompting, baseline evaluation, failure mode analysis, and MLflow experiment tracking.
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·2 min
From Demo to Production: Curing LLM Pipeline Drift with DSPy and MLflow
Learn how DSPy signatures and modules replace brittle hand-crafted prompts, and how MLflow tracking ensures reproducibility — systematically curing LLM pipeline drift from dev to production.
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Deep Dives·2 min
Agents Need Control Flow, Not More Prompts
Why do longer Prompts make AI Agents less stable? This article explains the control flow first architecture, replacing natural language control flow with code orchestration to boost multi-step reliability from 40% to over 90%.
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