Honestly it heavily depends on the use case, in terms of making the model better and choosing between RAG/FT. The most important thing to consider is what sort of changes you want to make to the model. FT is still a good choice if you’re looking for: strict output formatting (json/yaml/…) and refining for highly specific, narrow domain tasks. RAG is better for knowledge freshness, having source citations, and greatly lowers hallucinations.

RAG will inflate your context windows (more tokens) at inference time, so slower responses and requiring more energy at compute, whereas fine-tuning takes a ton of gpu compute up front (but retains smaller token counts at inference). If you’re doing 100,000 prompts a day, and only need to train once, FT makes more sense; if you’re doing 100 prompts a day and your knowledge database is constantly changing, RAG makes the most sense.

It’s hard to give a formalized estimate on energy efficiency: fine-tuning and getting to a certain training accuracy can take some undeterminate amount of time (and money on rented GPU compute), but could be a better choice if you think that up-front cost will be paid off over time if you use the model very frequently and only fine-tune once. On the other hand, going the RAG route will have an absolutely free up front compute (energy) cost, but be slightly more at compute time due to more tokens.

What’s your specific task you’re considering for FT or no FT? This is the most important thing to choose.