Unlocking the Potential of AI in the Build Pipeline

Artificial Intelligence (AI) has been making waves in various industries, but its potential in the build pipeline is not yet fully realized. In recent weeks, I have been experimenting with several applications and leveraging Language Model Machines (LLMs) to assist in coding tasks. While LLMs can flawlessly complete up to 95% of a task, they still fall short in certain areas. It begs the question – if they can recognize the mistake after it is pointed out, why couldn’t they get it right in the first place?

Despite this, build engineers are finding innovative ways to integrate LLMs into their workflows. From summarizing code for better documentation to using natural language search to identify bugs, LLMs are proving to be valuable assets. They can even refactor code to enhance reusability and maintenance and create more comprehensive test cases.

As LLMs continue to evolve, we are delving deeper into their reasoning abilities and limitations. We are exploring how they can enhance our code and absorb context effectively. While they are poised to revolutionize the build process, it will take time for these advancements to materialize. In the interim, human intervention remains essential in managing the different components of the build pipeline.

AI in the build pipeline is a fascinating journey filled with possibilities and challenges. As we navigate this new territory, we are redefining the roles of technology and human expertise in software development.