A personal project exploring why AI-generated SQL isn't always trustworthy and how semantic context, validation, and governance improve analytics accuracy.
AI-generated SQL can look right while being wrong. Learn how human-in-the-loop workflows build trust through reviews, approvals, audits, and escalation paths.
Run an offline Playground eval with a cross-family LLM judge and use failing rows to separate retrieval issues from generation problems and judge noise.
We optimized for code-generation speed while the real bottleneck — cognitive overhead and knowing where to make changes — remained completely untouched.
LLMs can quickly generate web application code, but AI-written code may contain security vulnerabilities. This article reviews testing methods for LLM systems.
This post traces that journey using triangular number computation as a practical example of intentional fall-through and connects the technique to Duff's Device.
Learn how to generate documentation using an LLM with mdship, and how to ensure that the prompts, which are now the source documentation, do not get lost.
Three structural shifts enterprise data security teams should make in 2026, based on verifiable data and a decade of experience building protection products.
If you’re a backend engineer working with AWS and curious about how we went from autocomplete-style AI to agentic, this one breaks down the architecture shifts.
Blockchain and data streaming are bringing unprecedented levels of security, transparency, and real-time mechanisms to move data across the digital world.