[charcircuit]

I imagine it is something similar to the following.

Preprocessing

1. Transcribe the dataset

2. Chunk the transcription into paragraphs.

3. Store the embedding of each paragraph into a vector database.

Querrying

1. Convert the user's query into an embedding

2. Query the vector database for the top N closest embeddings and fetch the paragraphs that correspond to them. To be robust against queries which you don't have results for you should limit how far away results can be from the user's query.

3. Using those paragraphs craft a propmt that you will give to a LLM.

4. Do any final filtering on the what you got back from the LLM.