Large Language Models (LLMs) are only as good as the data that shapes them. Whether you’re fine-tuning a model for domain-specific use or building an LLM-powered application from scratch, data readiness is the single biggest factor influencing performance, reliability, and ethical outcomes. As the saying goes, “garbage in, garbage out”  - but with LLMs, the cost of poor data goes beyond technical glitches; it can lead to bias, misinformation, and loss of trust. In this post,

While I’ve written on AI many times, I do often resist writing on the topic because I fear it is often overplayed and somewhat of a potential tech bubble. It won’t be going away, though, and even if its impact wanes, we still need to learn how to test and build quality around it. Especially in the realm of LLMs, which have had the largest impact on software development, both in how we create software and the types of solutions we can solve. Large language models (LLMs) like G

In my previous post,  we looked at the importance of focusing on access control and perimeter security. However, protecting the perimeter...

I’ve done several blog posts on modernization recently, with a big focus on transitioning older applications to be repurposed for the...