RAG does not help with training or fine tuning the model itself.
RAG just uses the retrieved information to disambiguate the context
and enrich the answer for one-time purpose of answering the question.
The model itself may be fine-tuned on how to summarise the result of
Retrieval systems to improve the quality of the answer.
However RAG does not teach any new facts to the trained model.
Advantages of RAG
Improved Accuracy: By providing latest domain-specific knowledge
Smaller Model Sizes: Because knowledge is offloaded to external
systems, rathern than memorizing vast knowledge.
Dynamic Adaptability: RAG allows the system to adjust to new
information without retraining the core model. (RAG does not retrain
the model just uses the retrieved knowledge to enrich answer)
Key Use Cases
Chatbots for customer support. Technical Q&A systems relying on
up-to-date documentation.
Personalized Applications: AI assistant based on use user-specific
data, like emails, documents, or settings.
Generating reports or summaries based on current, retrieved
information.
Note: The Model is already trained on the task of summarization.
Challenges of RAG
Depends on the quality of the retrieval system.
Latency: Retrieval system introduces Latency.
Hallucination Risk: The LLM might "hallucinate" or incorrectly
synthesize information.
Note: Model Deployments can not be standalone, requires Retrieval system
as well.