> Real-world data is mixed up, messy, and requires a lot of work to extract, clean, normalize, etc. Typically fine-tuned embedding model would be needed. I'd t

> Real-world data is mixed up, messy, and requires a lot of work to extract, clean, normalize, etc. Typically fine-tuned embedding model would be needed. I'd think the former is required for whichever strategy you choose, although I haven't found it necessary to fine-tune a base LLM. What use case are you thinking of when you say, "typically fine-tuning is required"?