RAG Explained: Fine-Tuning vs RAG

Disclaimer

If you are not familiar with RAG, I would suggest you to check out my following blog. Also I would highly recommend you to read the previous blogs of this series.

RAG Explained: Introduction, improving LLMs

Ďēv Šhãh 🥑 ・ Oct 1

#rag #langchain #llm #vectordatabase

Introduction

RAG and Fine Tuning, both are two primary methods to enhance the Large Language Models (LLMs) to respond more effectively to queries within a specific domain.

Fine-Tuning

Fine-Tuning involves training an existing LLM further on a specific dataset to enhance its expertise in a particular domain. This process allows the model to generate responses that align more closely with specialized knowledge within that domain. Once fine-tuning is complete, the model is primed to offer relevant responses.

Process Overview

The following visual illustrates the fine-tuning process. The model is first trained on domain-specific data, enabling it to generate responses tailored to that domain.

Retrieval-Augmented Generation (RAG)

The concept of RAG is already explained in this blog. However, in short, Retrieval-Augmented Generation (RAG) is a method where a language model retrieves relevant information from an external database in response to a user query, then combines this data with the prompt to generate an informed response.

Process Overview

Below is a visual representation of the RAG process. In a RAG setup, domain-specific data is transformed into vector embeddings and stored in a vector database with indexing to allow efficient retrieval. When a user submits a query, relevant data is retrieved from the vector database based on the query’s embeddings. The model then generates a response using this data, providing users with accurate, real-time information.

Note: Note: Concepts such as indexing, vector embeddings, and other foundational elements of RAG have been covered in previous posts in the 'RAG Explained' series.

Comparison

Feature	Fine-Tuning	Retrieval-Augmented Generation (RAG)
Method	The model is directly trained with the new data.	Data is retrieved dynamically based on the prompt.
Knowledge Update	Knowledge is fixed at the time of fine-tuning; retraining is required for updates.	Can utilize real-time or continuously updated data.

Each approach has its use case depending on the requirements for real-time data and domain specificity. Fine-tuning is beneficial when a model must have deeply ingrained, static knowledge of a domain. RAG, on the other hand, excels in scenarios where accessing the latest information is critical.

Citation
I would like to acknowledge that I took help from ChatGPT to structure my blog and simplify content.