ial to gauging the effectiveness of our retrieval.Retrieval is only half the story. The true test of our system is the quality of the final answers it generates by combining retrieved content with LLM intelligence. In the next section, we'll dive into a parallel evaluation process for the generation component, exploring both automated metrics and human assessment to get a well-rounded picture of our RAG pipeline's end-to-end performance. By shining a light on both halves of the RAG architecture, we'll be well-equipped to iterate and optimize our way to an ever more capable and reliable question-answering system.

Code Example

To explore the full code, visit the Complete Guide repository and for this section, particularly the eval_retrieval.py file.

PreviousEvaluation in 65 lines of code

NextGeneration evaluation

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