What are best practices for benchmarking AI editing performance in nonfiction projects to ensure quality and human alignment?

Question

Tyler Smith · Accepted Answer

Benchmarking AI editing performance in nonfiction is critical for ensuring the output meets the high standards of accuracy, clarity, and voice required for serious works. It's not enough to simply use an AI tool; one must systematically evaluate its effectiveness. A key best practice involves comparing AI-generated edits or suggestions against human expert judgment. This process should utilize 'low-tech solutions like spreadsheets to iterate on aligning model-based evaluation with human judgment,' allowing for detailed tracking of AI strengths and weaknesses across various editing parameters, such as factual accuracy, stylistic consistency, and adherence to developmental editing principles.

Furthermore, authors and editors should document and compare the rationale, performance benchmarks, and costs for choosing specific LLMs - whether open-source or proprietary - for their nonfiction projects. This helps in understanding which models excel at particular tasks, like identifying logical fallacies versus preserving a unique authorial voice. The process also includes defining clear quality metrics beforehand. For developmental editing, this might involve assessing improvements in argument structure or narrative flow. For voice preservation, it means evaluating how well the AI maintains the author's distinct tone and style post-edit. Ultimately, the goal is to 'make the final LLM output editable by a human within custom tools to curate and fix data for fine-tuning,' ensuring that human expertise remains the ultimate arbiter of quality and continuously improves the AI's future performance through fine-tuning data.