Why You Should Not Trust All the Numbers You See

The text critiques the effectiveness of certain metrics used to evaluate AI code assistants, emphasizing that statistics like acceptance rates and percentages of AI-generated code can be misleading due to the unique nature of software development across different contexts. It suggests that qualitative feedback and detailed analytics dashboards that provide transparency are more valuable for assessing the real impact of these tools for individual users and enterprises. The text also describes an evaluation method for an autocomplete language model that involves using public repositories to find and test functions, simulating the completion of deleted snippets, and running unit tests to assess performance. It argues for a data-driven approach in rollout processes and underscores the importance of balancing various metrics, such as latency and bytes completed, to ensure that users derive more value from the evolving autocomplete system. It concludes with a promise to address further questions in a follow-up blog post.

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