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Beyond Summarization: Extracting Critical Fields that Matter for Insurance Decisions
Insurance teams often ask LLMs to summarize documents, then struggle to define what a good summary looks like. A better approach is to start from the decision, extract the critical fields, preserve their evidence, and use narrative generation only where it adds value.
From Demo to Production: Four Hard-Earned Lessons from Shipping GenAI for Insurance Claims
Four hard-earned lessons from turning a promising GenAI claims demo into a production-ready solution—from defining meaningful measurements and managing technical debt to building effective engineering and domain-expert feedback loops.
OCR Evaluation, Rebuilt for Insurance Claims in GenAI Era
Processing scanned claim PDFs used to be “good enough” if the text was mostly readable. In the GenAI era, that’s no longer true: if OCR misses a checkbox, scrambles a table, or loses layout context, the document becomes unreliable for extraction, retrieval, and automated decisions. Insurance makes this worse—many documents are old, low-quality scans with handwriting and dense forms—so we need evaluation that reflects real usability, not just character-perfect transcription.
DSPy, Meet Enterprise: Plugging in an Internal LLM Endpoint
DSPy is powerful, but many enterprise environments can’t call public LLM endpoints directly. This guide shows a minimal, practical way to plug an internal (non-standard) LLM service into DSPy by writing a custom dspy.BaseLM adapter.
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