One of the most intriguing features of large language models is their potential to increase output through self-reflection. Feed the model its own response and then ask it to enhance it or discover faults, and it has a much better chance of providing something factually correct or appealing to its consumers. Ask it to solve a problem by demonstrating its work step by step, and these systems are more accurate than those geared solely for the correct final response.

While the area is still rapidly evolving, and factual errors, often called as hallucinations, continue to plague many LLM-powered chatbots, a growing body of research suggests that a more directed, auto-regressive approach can yield better results.

This becomes particularly intriguing when applied to the world of software development and CI/CD. Most developers are already aware of automated methods for code production, issue identification, solution testing, and idea documentation. Several have already written about the concept of self-healing code. Head over to Stack Overflow's CI/CD Collective for countless examples of technologists putting these ideas into action.

When code fails, it usually produces an error message. If your program is any decent, the error message will explain what went wrong and link you to a solution. Previous self-healing code programs were smart automations that reduced failures, provided graceful fallbacks, and managed notifications.

What are people creating and experimenting with today?

Google is already utilizing this technology to expedite the process of resolving code review comments. The authors of a recent study using this approach note that, "As of today, code-change authors at Google address a significant amount of reviewer comments by applying an ML-suggested edit." We anticipate that this will cut the time spent on code reviews by hundreds of thousands of hours per year at Google size. Unsolicited, extremely good feedback indicates that the impact of ML-suggested code adjustments boosts Google productivity and allows them to focus on more creative and complicated activities.

"In many cases when you go through a code review process, your reviewer may say, please fix this, or please refactor this for readability," says Marcos Grappeggia, the project manager for Google's Duet coding assistance. He envisions an AI agent that can react to this as an advanced linter for evaluating remarks. "That's something we saw as being promising in terms of reducing the time for this fix getting done." The proposed solution does not replace a person, "but it helps, it gives you kind of a starting point to think from."

Recently, we've seen some exciting experiments that use this review power to test code that you're about to deploy. 

How is Stack Overflow experimenting with GenAI?

As our CEO recently revealed, Stack Overflow now has an internal team committed to researching how AI, namely the latest wave of generative AI and the field as a whole, might improve our platforms and products. We plan to build in public so that we may incorporate feedback into our process. In that spirit, we released an experiment that assisted users in creating an appropriate title for their query. The purpose here is to make life easier for both the question asker and the reviewers while also encouraging everyone to engage in the knowledge exchange that takes place on our public website.

It's easy to conceive a more iterative procedure that takes advantage of multi-step prompting and chain of thought reasoning, both of which have been shown in research to significantly improve the quality and accuracy of an LLM's output.

An AI system may examine a question, make changes to the title for legibility, and offer suggestions for better formatting code in the question's body, as well as a few extra tags at the end to improve classification. Another system, the reviewer, would check over the modified question and give it a score. If it meets a specified threshold, it may be returned to the user for review.