A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence

This article explains three key Graph Neural Network architectures - GCN, MPNN, and GAT - with practical applications in molecular science, social networks, and traffic analysis.

When fuzzy matching algorithms like Damerau-Levenshtein fail to distinguish typos from legitimately similar product identifiers, requiring alternative approaches for entity reconciliation.

PySpark window functions enable aggregations like rankings and running totals while preserving individual row details.

Structured outputs can produce valid JSON with incorrect data when fields are missing from source text, creating silent data corruption issues that bypass validation.

A practical guide to implementing Bayesian Neural Networks in Python that provides uncertainty quantification instead of just point predictions.