The solution proposed is backtesting, a method of testing AI by replaying historical scenarios with new settings. This allows for a rigorous evaluation of how an AI feature would perform before it is exposed to customers. The article highlights Reva's backtesting platform, which provides data-driven insights and end-to-end confidence for teams to ensure their AI systems perform as expected in real-world scenarios.
Key takeaways:
- Large Language Models (LLMs) pose a significant challenge in evaluation due to their versatility and non-deterministic nature, making conventional ML evaluation metrics nearly impossible to apply.
- The two main issues in evaluating LLMs are Capability and Alignment. Capability refers to the breadth of tasks LLMs can be applied to, and Alignment refers to the gap between capability and consistency in real-world use.
- Backtesting is a solution for testing AI models, by replaying a historical scenario with new settings, it allows for a rigorous evaluation of how the AI feature would perform before exposing it to customers.
- Reva provides a scalable backtesting platform that allows for meticulous testing across a wide range of scenarios, optimizing model features, and delivering AI solutions that meet the needs of customers.