You can improve generative AI response accuracy by using a neural reranker that scores multiple candidate responses and selects the most relevant one.
Here is the code snippet below:

In the above code we are using the following key points:
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Fine-tuning a BERT-based reranker model to score relevance between query and candidates
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Tokenizing and scoring each candidate relative to the query
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Selecting the highest scoring candidate as the output
Hence, neural rerankers refine generative AI outputs by prioritizing relevance and context matching more effectively.