An open source framework for building AI-powered apps with familiar code-centric patterns. Genkit makes it easy to develop, integrate, and test AI features with observability and evaluations. Genkit works with various models and platforms.
This text reveals several areas where the documentation for Genkit, particularly around evaluation, could be improved:
* **Clarify how evaluators are standardized.** The text acknowledges that while evaluation metrics like Faithfulness and Answer Relevance are becoming standardized, their implementation can vary. The documentation should provide more concrete information on this, perhaps by:
* Giving specific examples of how implementations can differ.
* Offering guidance on choosing the best implementation for different use cases.
* Explaining how Genkit handles these variations to ensure consistency.
* **Provide more guidance on quantifying output variables.** The text mentions that users can define custom evaluation metrics, but it should offer more support on how to do this effectively. Consider adding:
* Examples of quantifying different types of outputs.
* Best practices for designing custom metrics.
* A step-by-step guide to implementing custom evaluators.
* **Expand on the scope of pre-defined evaluators.** Users need a clearer understanding of what metrics like "Maliciousness" actually measure. The documentation should:
* Provide detailed explanations of each pre-defined metric.
* Clarify which RAGAS metrics are included in Genkit.
* Offer examples of how these metrics are used in practice.
* **Improve the description of "Maliciousness"**. The current explanation is vague. The documentation should clearly define what constitutes "maliciousness" in the context of LLMs and how the evaluator identifies it.
* **Clarify the analogy to testing.** While the text likens evaluators to E2E testing, it could be more explicit about how they fit into the development process. This could involve:
* Explaining when and how to use evaluators during development.
* Providing examples of how evaluators can help identify regressions.
* Discussing how evaluators can be integrated into a CI/CD pipeline.
By addressing these points, the documentation can better support users in understanding and effectively using Genkit's evaluation features.
Autogenerated from Gemini:
Context: https://discord.com/channels/1255578482214305893/1281391213550895124/1282325935038926868