Fine-tuning Vs Embedding for Question Answering

Hi,
I am looking to develop a question answering system to work on structured data. I have the option of either using the Embeddings endpoint to store the data as vectors and then use embeddings similar to embeddings of the query to select the top-k results to be returned and use a verification method to get the correct answer, or use fine-tuning to train the model to the data and then make use of the fine-tuned model.

Considering that the data could change with time, can someone please guide me how to decide which alternative would be best to use?

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Embeddings work well if the length of the text is not short.

Otherwise, a traditional full-text DB search works better on short strings.

HTH

:slight_smile:

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