ai-engineer-rag-implementation-rag-vs-fine-tuning

# ai-engineer-rag-implementation-rag-vs-fine-tuning

## Contents

__Roadmap info from [ roadmap website ] (https://roadmap.sh/ai-engineer/rag-vs-fine-tuning@qlBEXrbV88e_wAGRwO9hW) __

  ## RAG vs Fine-tuning

  RAG
  (Retrieval-Augmented Generation)
  and
  fine-tuning
  are
  two
  approaches
  to
  enhancing
  language
  models, but they differ in methodology and use cases. Fine-tuning involves training a pre-trained model on a specific dataset to adapt it to a particular task, making it more accurate for that context but limited to the knowledge present in the training data. RAG, on the other hand, combines real-time information retrieval with generation, enabling the model to access up-to-date external data and produce contextually relevant responses. While fine-tuning is ideal for specialized, static tasks, RAG is better suited for dynamic tasks that require real-time, fact-based responses.