If possible, look at the training set on which the model was trained. My understanding is that you can better elicit the model’s capabilities if you follow a similar structure to what the model was trained on. If you don’t have access to the dataset (like is often the case, even though some people pretend to be ‘open source’), then look at the prompt guides of the company that released the model. However, you can still try to predict the data distribution to see if you can outperform what the company puts out there.
I’d like to add:
An example of “Fake RL”/well-prompted LLM: When trying to prompt base models, you can look at methods like URIAL (The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning), which apparently performs similarly to RLHF on benchmarks.
If possible, look at the training set on which the model was trained. My understanding is that you can better elicit the model’s capabilities if you follow a similar structure to what the model was trained on. If you don’t have access to the dataset (like is often the case, even though some people pretend to be ‘open source’), then look at the prompt guides of the company that released the model. However, you can still try to predict the data distribution to see if you can outperform what the company puts out there.