The article also highlights the role of Parea, a tool that simplifies the process of instrumenting and testing each step, as well as creating performance reports. Parea also provides a cache for Language Model (LLM) calls, which can speed up the iteration time and reduce costs. The author concludes by summarizing the key tactics: testing every sub-step to minimize cascading effects of failure, using reference-based evaluation for individual components, and caching LLM calls to speed up and save costs when iterating on independent sub-steps.
Key takeaways:
- Testing every sub-step in multi-component AI apps is crucial to minimize the cascading effect of their failure, with a 90% accuracy in each step resulting in a 60% error for a 10-step application.
- Reference-based evaluation, using production logs or synthetic data, is a more grounded and easier method for testing sub-steps in AI applications.
- Caching Language Model (LLM) calls can speed up iteration time, reduce costs, and lead to deterministic behaviors in AI apps, simplifying testing.
- Parea can assist in these tactics by simplifying the process of instrumenting and testing steps, creating reports on component performance, and acting as a cache for LLM calls.