Choosing an LLM adaptation method

We have covered various LLM adaptation methods, including prompt engineering, domain adaptation pre-training, fine-tuning, and RAG. All these methods are intended to get better responses from the pre-trained LLMs. With all these options, it leaves one wondering: how do we choose which method to use?

Let’s break down some of the considerations when choosing these different methods.

Response quality

Response quality measures how accurately the LLM response is aligned with the intent of the user queries. The evaluation of response quality can be intricate for different use cases, as there are different considerations for evaluating response quality, such as knowledge domain affinity, task accuracy, up-to-date data, source data transparency, and hallucination.

For knowledge domain affinity, domain adaptation pre-training can be used to effectively teach LLM domain-specific knowledge and terminology. RAG is efficient in retrieving...