  Retrieval-augmented generation resorts to content retrieved from external
sources in order to leverage the performance of large language models in
downstream tasks. The excessive volume of retrieved content, the possible
dispersion of its parts, or their out of focus range may happen nevertheless to
eventually have a detrimental rather than an incremental effect. To mitigate
this issue and improve retrieval-augmented generation, we propose a method to
refine the retrieved content before it is included in the prompt by resorting
to meta-prompting optimization. Put to empirical test with the demanding
multi-hop question answering task from the StrategyQA dataset, the evaluation
results indicate that this method outperforms a similar retrieval-augmented
system but without this method by over 30%.
