Provides a ready-to-use configuration for power users to enforce consistent coding standards and reduce repetitive prompting when working with AI code assistants.
Author shares their agent.md file used to AI code assistants like Claude, containing detailed coding style rules that dramatically improve LLM-generated code quality.
Details a system that intelligently routes risky AI-generated SQL queries for human review while maintaining throughput, based on real incidents.
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.
Structured outputs can produce valid JSON with incorrect data when fields are missing from source text, creating silent data corruption issues that bypass validation.
Explores how typos, transcription noise, and OCR errors break RAG systems and compares classical spell-check solutions with embedding-based approaches.
