Nowadays, we don’t have to introduce ChatGPT anymore. Large Language Models (LLMs), - the power behind tools like ChatGPT - are transforming the way businesses operate by leveraging AI to understand both imagery and natural language and to generate suitable output. These models have been pre-trained on vast and varied data sets, giving them a varied diverse content knowledge. In addition they have been designed to do a simple task - predicting the most likely next word. The combination of these two characteristics provide them with the capability to handle a broad range of tasks, hence making them into the successful example of Generative Foundation models.
For the purpose of explaining things at an understandable level, we represent the LLM as a high school student.
Without going into technical details here, we want to underline that in the finetuning case, you are changing the actual capacities of the LLM/student. You go further than just providing him “examples ad-hoc” like you do for the few-shot prompting case. Instead, you present the student with input tasks and correct his output based on the “correct” outputs that your fine-tuning dataset describes. One could see this as “taking the student to the next level”: allowing the student to specialise within the domain that your dataset implicitly describes. The analogy between a high school graduate and a university graduate is then straightforward.
In conclusion, we note that for many business cases, the “few-shot prompting” approach may suffice to get your model to behave appropriately (e.g. when you are looking for a conversational LLM that replies based on information extracted from your knowledge base). In other cases, however, some specific behaviour of your model may require fine-tuning of your model in order to make it perform better.
If you want a practical example on how a RAG structure is build up and incorporated in use cases, you can rewatch our webinar on demand on Generative AI for corporate use: How to get started with LLM.