Building a Proper Backend for My LangGraph AI Agent

Details a system that intelligently routes risky AI-generated SQL queries for human review while maintaining throughput, based on real incidents.

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.

Presents an architectural approach for optimizing RAG systems when the source corpus is a curated FAQ, showing how to leverage pre-structured question-answer pairs for better performance.

Explores how typos, transcription noise, and OCR errors break RAG systems and compares classical spell-check solutions with embedding-based approaches.