My approach was not to train the model on the documents, as others mentioned. I built a vector database from the documents, and I query the questions against i

My approach was not to train the model on the documents, as others mentioned. I built a vector database from the documents, and I query the questions against it, which is very fast. This is the RAG (retrival augmented generation) step others mentioned. The results, which are literal extracts from the documents, but short ones, are given to the model, which produces an answer. This is the slow part. I used many of Langchain's tools to manage the whole process. You can try it on Supawiki, with one of the featured wikis. Then, if you are ok with a solution that hosts your documents for you, you can upload them and use our solution.