It’s not yet as obvious how AIs can help with the build pipeline. In the last few weeks, I’ve been iterating on several applications while asking various LLMs to write the code. While they’re often able to do up to 95% of a task perfectly, they still get several things wrong. When I point out the problem, the LLMs respond very politely, “You’re absolutely right …” If they know it after I point it out, why didn’t they know it beforehand? Why couldn’t they finish the last 5% of the job?
That’s a question for the future. For now, build engineers are finding other ways to use LLMs. Some are summarizing code to produce better high-level documentation. Some are using natural language search to ask an AI companion where a bug started. Others are trusting LLMs to refactor their code to improve reusability and maintenance. One of the most common applications is creating better and more comprehensive test cases.
LLMs are still evolving, and we’re still understanding how well they can reason and where they are likely to fail. We’re discovering just how much context they can absorb and how they can improve our code. They will add more and more to the build process, but it will be some time before those improvements appear. Until then, we’re going to need to manage how the parts come together. In other words, we humans will still have a job maintaining the build pipeline.
