The article further discusses the importance of 'vibes-based' evaluations, where users try the model out and assess the quality of responses. While this method may not provide empirical data, it is effective in building or losing confidence during an evaluation. The author concludes by emphasizing the need for product builders to prove their product's value to customers, and the need for developing product- and task-specific measures to build customer confidence.
Key takeaways:
- The most successful customers are those who have previously tried to build a custom AI assistant, as they understand the potential failure modes and can recognize higher quality responses more quickly.
- The quality and specificity of data is the most important factor in determining the quality of AI responses. Improving data processing is often the simplest solution to improving the quality of responses.
- Managing expectations for AI products is challenging, with two main groups of people being dissatisfied: those who see AI as a novelty and those who expect it to perform perfectly.
- 'Vibes-based' evaluations, where the product is tested and evaluated based on the quality of its responses, have become the dominant method of evaluating AI products, despite not being an empirical solution.