Poor results with fine tuning gpt-4o-mini

[PaulBellow]

How to fine-tune gpt3.5-turbo to give it new knowledge?

Welcome to the community, @brandojazz! I agree with @anon22939549. Fine-tuning is recommended to save on prompt tokens for a high volume of calls for specific use case(s) or to set model behavior. For knowledge augmentation and retrieval, embeddings is the go-to approach. Here’s the documentation on embeddings. Currently, GPT models have a finite context length, which limits the number of tokens (prompt + completion) they can handle. To overcome this, you can pass an outline of your reposi…