Part 2/8:
The first lesson highlights the importance of using a systematic evaluation process to measure AI model performance against specific use cases. OpenAI underscores that evaluation should involve rigorous testing and validation. Successful evaluations could lead to more stable and reliable AI applications.
For instance, Morgan Stanley, which is quoted in the report, focused on three evaluation metrics: language translation accuracy, summarization quality, and human trainer comparisons of AI outputs. This rigorous approach instills confidence in the deployment of AI tools across the organization.