Hi Marco,

Thank you for your thoughtful insights on the signal-to-noise ratio issue.

You've hit on an essential point about the need for balance between specificity and broad exploration.

To address this, i've integrated adjustable parameters like 'k' in Reciprocal Rank Fusion (RRF) that allow us to fine-tune the noise-to-signal ratio.

This helps in delivering specific results when needed, without sacrificing the model's capability for broader knowledge exploration.

Moreover, while the model may generate an expanded list of results, the aim is for the final re-ranked list to be the most relevant and informative set of results possible.

The process involves initially focusing on directly answering the primary query and then supplementing that answer with additional, relevant information based on re-ranked generative queries.

Keen to continue this discussion to better understand your perspective.

Best,

Adrian