Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks

This article explores the efficient frontier concept for LLM inference optimization, explaining how to balance latency vs throughput tradeoffs and techniques to push the entire efficiency frontier outward.

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

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.

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

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