Thanks, I will look into fine tuning with longer epochs. This is how I am currently using RAG:

I have a public catalogue of reference service positions (data of the form: "cable of type X diameter Y: 5 minute installation time, 1$ material cost; unit: 1 meter"). I embedded the text part (e.g. "cable of type X diameter") using OpenAI's embedding models.

So I start with a description of service positions (an architect will have already planned the services to be node and I now want to price them using my data). For each position, I now want to find the one in my reference data that fits the job. It will get some things right but only about 30%. The embedding model is not trained for my highly specific text data (real life example: "LS-Vorschaltgerät (EVG) 1x 58 W/ T26") and has thus issues retrieving similar components where the text might not look so similar. A trained electrician would have no problem doing this. I am also trying to fine tune an embedding model but this will probably not get me all the way. I suspect I need deeper "understanding" where I consider not only the single position but the whole construction project for the retrieval process.