What GitHub Copilot Lacks: Fine-tuning on Your Private Code

A lawyer using ChatGPT to submit briefs with fabricated precedent cases highlights the importance of providing accurate context to AI models, particularly when dealing with private data or recent information. Context is crucial for applications like code autocompletion, such as GitHub Copilot or Codeium, which handle context collection differently from ChatGPT, as they are limited by cost and latency constraints to processing around 150 lines of code. This limitation can lead to hallucinations of entire schemas or utility functions, raising doubts about the efficacy of such solutions for private codebases. However, fine-tuning models like Codeium for private repositories can reduce these hallucinations and improve performance, surpassing competitors like GitHub Copilot. To determine the value of a generative AI solution for software development, especially for private repositories, it is essential to ensure that it offers more benefits than the potential time spent debugging.

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