As we move software products using generative AI technology from proof-of-concepts into production systems, we are uncovering a range of common patterns. Evals play a central role in ensuring that these non-deterministic systems are operating within sensible boundaries. Large Language Models need enhancement to provide information beyond a generic and static training set. Most of the time we can do this with Retrieval Augmented Generation (RAG), although the basic RAG approach requires several patterns to overcome its limitations. When RAG isn't enough, Fine Tuning becomes worthwhile.

I was on a panel at goto Copenhagen with Holly Cummings, Trisha Gee, Dave Farley, and Daniel Terhorst-North. We discussed the current state of software development and where it was heading. Given the timing, there was much discussion about the role AI would play in our profession's future.

Luca Rossi hosts a podcast (and newsletter) called Refactoring, so it's obvious that we have some interests in common. The tile comes from me leaning heavily on Beth Anders-Beck and Kent Beck's metaphor of The Forest and The Desert. We talk about the impact of AI on software development, the metaphor of technical debt, and the current state of agile software development.

Refactoring is something developers do all the time—making code easier to understand, maintain, and extend. While IDEs can handle simple refactorings with just a few keystrokes, things get tricky when you need to apply changes across large or distributed codebases, especially those you don’t fully control. That’s where codemods come in. By using Abstract Syntax Trees (AST), codemods allow you to automate large-scale code changes with precision and minimal effort, making them especially useful when dealing with breaking API changes. This article looks at how codemods can help manage these challenges, with practical examples like removing feature toggles or refactoring complex React components. We’ll also discuss potential pitfalls and how to avoid them when using codemods at scale.