As organizations are looking for ways to make large language models (LLMs) work in the context of their product, domain or organizational knowledge, we're seeing a rush to fine-tune LLMs. While fine-tuning an LLM can be a powerful tool to gain more task-specificity for a use case, in many cases it’s not needed. One of the most common cases of a misguided rush to fine-tuning is about making an LLM-backed application aware of specific knowledge and facts or an organization's codebases. In the vast majority of these cases, using a form of retrieval-augmented generation (RAG) offers a better solution and a better cost-benefit ratio. Fine-tuning requires considerable computational resources and expertise and introduces even more challenges around sensitive and proprietary data than RAG. There is also a risk of underfitting, when you don't have enough data available for fine-tuning, or, less frequently, overfitting, when you have too much data and are therefore not hitting the right balance of task specificity that you need. Look closely at these trade-offs and consider the alternatives before you rush to fine-tune an LLM for your use case.
