DSPy Prompt Automation Pipeline

Production prompt optimization system at Senren using DSPy for programmatic prompt engineering. Enforces strict JSON schemas for structured data extraction across NLP services.

Context

At Senren, experimental NLP research needed to become production-reliable. Manual prompt engineering doesn't scale — prompts break when models update, edge cases surface constantly, and output format compliance is inconsistent.

Approach

DSPy Integration: Architected automation logic using DSPy to refine prompts programmatically, replacing manual iteration with optimizable modules
Schema Enforcement: Strict JSON schema validation on all LLM outputs, ensuring structured data extraction meets downstream system requirements
Model Selection Framework: Built rigorous trade-off analysis pipeline evaluating models (Gemini 2.5 series, GPT-4o/4.1) on context window, latency, cost, and output accuracy

Engineering Leadership

Lead the transition of experimental NLP research into production
Coordinate cross-stack integration (Frontend/Backend/Infra)
Own the decision-making process for model adoption across the platform

Impact

Reduced prompt iteration cycles from days to hours through programmatic optimization
Achieved consistent JSON schema compliance across production NLP services
Enabled reliable AI service deployments with automated regression testing