A typed answer schema forces the RAG model to fill predefined fields rather than free‑form text, turning every response into a checkable contract. This eliminates hallucinations by ensuring answers are grounded in retrieved passages and can be automatically validated, streamlining enterprise document intelligence pipelines.
Long, cluttered prompts flood LLMs with irrelevant details, causing the model to miss crucial context and degrade reasoning performance. The blog shows that even well‑below token limits, excess length hampers output quality, and suggests using meta‑prompting, another LLM that trims prompts, as a practical fix.
Relying on raw accuracy can hide a model's real reliability, especially when decisions depend on calibrated probabilities. This piece walks through practical alternatives, calibration curves, Brier score, discrimination metrics, for both classification and regression, showing how they reveal hidden biases and guide safer deployments.