Specifically you can show a T5 model input and output texts and it will try to learn the transformation between them. People tell me T5 models are relatively easy to train and they perform well on many tasks.
If you have some specific documentation on your topic you could use the embedding to find some text that is relevant to the query. In fact this stacks great with the fine-tuning because you could train the model to, given a question and a relevant document, give an answer. T5 is good at that kind of basically summarization task.
https://huggingface.co/docs/transformers/en/model_doc/t5
https://medium.com/nlplanet/a-full-guide-to-finetuning-t5-fo...
Specifically you can show a T5 model input and output texts and it will try to learn the transformation between them. People tell me T5 models are relatively easy to train and they perform well on many tasks.
Note another approach to your problem is RAG
https://www.promptingguide.ai/techniques/rag
If you have some specific documentation on your topic you could use the embedding to find some text that is relevant to the query. In fact this stacks great with the fine-tuning because you could train the model to, given a question and a relevant document, give an answer. T5 is good at that kind of basically summarization task.