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.
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.
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 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.
In this post, I explore how modern web apps like RAGflow handle long-running tasks (such as OCR or keyword extraction) without relying on time-limited HTTP API methods. Instead, tasks are processed in the background by worker processes, with progress updates communicated to the frontend via Redis. I demonstrate this design with a simple app built using FastAPI, Vue 3, and Redis, where tasks are submitted, processed in the background, and their statuses are periodically updated on the frontend...