One thing that stands out while working with RAG systems is how much influence the retriever has on the final answer.
A common assumption is that improving the model will automatically improve the results. In reality, the model can only work with the context it receives.
If the retriever brings back irrelevant documents, incomplete information, or misses the most relevant content entirely, even a powerful LLM will struggle to produce a useful response.
That's what makes retrievers so important.
Their job isn't to generate answers. Their job is to find the right information at the right time.
In many RAG pipelines, improving retrieval quality can have a bigger impact than switching to a larger model.
The model gets most of the attention, but the retriever often decides whether the answer has a chance of being correct in the first place.
Have you found retrieval quality or model quality to be the bigger bottleneck in your RAG applications?
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