SFT Supervised Fine Tuning Vs. RAG And Prompt Engineering
Large language model (LLM) development frequently starts with pre-training. The model gains general language comprehension at this stage by reading vast volumes of unlabeled material
Full fine-tuning: modifies every parameter in the model. However, comprehensive fine-tuning results in greater total costs because it requires more computer resources for both tuning and serving
Parameter-Efficient Fine-Tuning (PEFT): In order to facilitate quicker and more resource-efficient fine-tuning, a class of techniques known as Parameter-Efficient Fine-Tuning
Supervised Fine Tuning helps the model become proficient at the job, which minimizes the need for long and intricate cues during inference
In order to increase quality and accuracy, Retrieval Augmented Generation (RAG) gathers pertinent data from Google search and other sources and gives it to the LLM
Supervised Fine Tuning can be combined with other methods you may already be attempting to create more effective models
Prompt engineering is affordable, accessible, and simple to use for controlling outputs. For managing intricate or subtle jobs, it could be less dependable and necessitates experience and trial