Databricks Intern Teaches AI to Enhance Code Quick Fix

Just as humans refine skills through iterative practice and learning from mistakes, artificial intelligence models are now being trained to perfect their craft in complex domains like code repair. This fundamental feedback loop was at the heart of a recent summer internship project at Databricks, aimed at elevating the capabilities of its Code Assistant. The initiative focused on teaching a specialized “reward model” to discern and prioritize optimal code fixes, a crucial step in building a sophisticated developer tool.

The core innovation is Databricks’ “Quick Fix” feature, seamlessly integrated into its Notebooks and SQL Editors. Designed for rapid, high-confidence resolutions, Quick Fix targets common issues such as syntax errors, misspelled column names, and straightforward runtime problems. When a developer encounters an error, Quick Fix springs into action, analyzing the problematic code and its accompanying error message. It then leverages a large language model (LLM) to swiftly generate a targeted solution, typically within one to three seconds.

While Quick Fix already provided valuable assistance, the challenge lay in enhancing its precision. Even after a fix was generated and passed basic syntax checks, how could the system guarantee it was the most relevant and accurate solution for the user? The answer emerged through a technique known as “best-of-k sampling.” This approach involves generating multiple potential fix suggestions, then employing a sophisticated reward model to evaluate and select the single best option.

The project encompassed both backend engineering and experimental research. The initial phase focused on expanding the Quick Fix system to generate a diverse array of suggestions in parallel. This involved experimenting with various prompts and contextual information, including techniques like “chain-of-thought” reasoning, predicted output reasoning, and variations in system prompts, alongside selective database context. While these methods boosted the quality and variety of suggestions, they also introduced a degree of processing delay, highlighting a critical trade-off between output quality and operational speed.

Once multiple suggestions were generated, the next step was to identify the most promising one to present to the user. An early attempt involved a simple majority voting system, which, despite performing well in isolated tests, did not yield significant improvements in real-world user A/B testing and was thus not rolled out. The more effective solution involved developing and training dedicated reward models. These models were engineered to predict which proposed fixes users would ultimately accept and successfully execute. Interestingly, the development process utilized both traditional machine learning methods, such as logistic regression and gradient-boosted decision trees (specifically using the LightGBM package), alongside more contemporary fine-tuned large language models.

A notable finding emerged from the evaluation: for the specific task of predicting user acceptance and execution success, the classical machine learning models performed comparably to the more resource-intensive fine-tuned LLMs in offline assessments. The decision tree model, in particular, proved highly effective. Its success was attributed to the fact that for the types of errors Quick Fix addresses, code edits that appear correct often are. Key features that informed its decisions included the similarity between the original line of code and the generated fix, as well as the specific error type encountered.

Given its comparable performance and, crucially, its significantly faster inference time, the LightGBM decision tree model was ultimately chosen for deployment into production. Speed is paramount for Quick Fix; suggestions must appear almost instantly, before a developer can manually intervene, as any delay reduces the number of errors that can be swiftly resolved. The compact size of the LightGBM model also made it remarkably resource-efficient and easier to integrate into the existing infrastructure. Through a combination of model and infrastructure optimizations, the average inference time was dramatically reduced by nearly a hundredfold. This implementation of the best-of-k approach, coupled with the reward model, led to a measurable increase in Databricks’ internal acceptance rate, directly translating to higher quality and a more seamless experience for users, all while keeping system latency well within acceptable bounds.