 total_tests) * 100

return round(failure_rate, 2)When we run this as part of the evaluation pipeline, we get a 16% failure rate which again tells us that we're doing pretty well but that there is room for improvement. As a baseline, this is a good starting point. We can then iterate on the retrieval component to improve its performance.

To take this further, there are a number of ways it might be improved:

More diverse question generation: The current question generation approach uses a single prompt to generate questions based on the document chunks. You could experiment with different prompts or techniques to generate a wider variety of questions that test the retrieval component more thoroughly. For example, you could prompt the LLM to generate questions of different types (factual, inferential, hypothetical, etc.) or difficulty levels.

Semantic similarity metrics: In addition to checking if the expected URL is retrieved, you could calculate semantic similarity scores between the query and the retrieved documents using metrics like cosine similarity. This would give you a more nuanced view of retrieval performance beyond just binary success/failure. You could track average similarity scores and use them as a target metric to improve.

Comparative evaluation: Test out different retrieval approaches (e.g. different embedding models, similarity search algorithms, etc.) and compare their performance on the same set of queries. This would help identify the strengths and weaknesses of each approach.

Error analysis: Do a deeper dive into the failure cases to understand patterns and potential areas for improvement. Are certain types of questions consistently failing? Are there common characteristics among the documents that aren't being retrieved properly? Insights from error analysis can guide targeted improvements to the retrieval component.