[metawake]

Author here. Built RagTune to stop guessing at RAG configs.

Surprising findings:

1. On legal text (CaseHOLD), 1024 chunks scored WORST (0.618). The "small" 256 chunks won (0.664). 7% swing.

2. On Wikipedia text? All chunk sizes hit ~99%. No difference.

3. Plot twist: At 5K docs, optimal chunk size FLIPPED from 256→1024. Scale changes everything.

Code is MIT: github.com/metawake/ragtune

Happy to discuss methodology.

[patrakov]

Now that you have 5K docs, can you try estimating the statistical uncertainty of the Recall@5 and MRR metrics measured via smaller datasets? Just make some different 400-document subsets of the whole 5K HotpotQA dataset and recalculate the metrics.

[metawake]

Great suggestion!! this is exactly the right methodology for establishing confidence intervals.

I've added this to the roadmap as `--bootstrap N`:

    ragtune simulate --queries queries.json --bootstrap 5
    
    # Output:
    # Recall@5:  0.664 ± 0.012 (n=5)
    # MRR:       0.533 ± 0.008 (n=5)

The implementation would sample N random subsets from the query set (or corpus), run each independently, and report mean ± std.

This also enables detecting real regressions vs noise eg "Recall dropped 3% ± 0.8%" is actionable, "dropped 3%" alone isn't.

Will ship this during next few weeks. Thanks for the push toward more rigorous methodology, this is exactly what's missing from most RAG benchmarks.