The article also provides Python code examples for each metric and suggests ways to implement these metrics through Parea. It emphasizes the importance of distinguishing between end-to-end and step/component-wise evaluation when assessing LLM applications. The insights provided in the article are based on research literature and discussions with other LLM app builders.
Key takeaways:
- When building LLM applications, it's crucial to implement quality control and evaluation metrics to catch undesired model behaviors and improve overall quality.
- There are different evaluation metrics for different scenarios, including general purpose evaluation metrics, RAG specific evaluation metrics, AI assistant/chatbot-specific evaluation metrics, and evaluation metrics for summarization tasks.
- These metrics can be used to rate LLM calls, assess the relevance of generated responses, detect hallucinations, evaluate the relevance of context to queries, assess the faithfulness of generated answers, and measure the quality of chatbot interactions and text summaries.
- These evaluation metrics can be implemented individually or through platforms like Parea, which provides an onboarding wizard and a dashboard for viewing logs.