Fine-Tuning LLMs With Retrieval Augmented Generation (RAG)

This approach is a novel implementation of RAG called RA-DIT (Retrieval Augmented Dual Instruction Tuning) where the RAG dataset (query, context retrieved and response) is used to to fine-tune a LLM…

RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?, by Heiko Hotz

Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs

Leveraging LLMs on your domain-specific knowledge base, by Michiel De Koninck

RAG vs Fine-Tuning: Choosing the Best Tool for Your LLM, by Abhishek Ranjan

Fine Tuning or Retrieval Augmented Generation (RAG), That Is the Question, by Peng Liu, Mar, 2024

List: LLM, Curated by CP Lu, PhD

Enrich LLMs with Retrieval Augmented Generation (RAG), by Murtuza Kazmi

Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation, by Leonie Monigatti

RAG vs Fine-Tuning: Choosing the Best Tool for Your LLM, by Abhishek Ranjan

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Fine Tuning or Retrieval Augmented Generation (RAG), That Is the Question, by Peng Liu, Mar, 2024

List: RAG methods, Curated by Pradeep Mohan

LLMs, RAG, and Fine-Tuning: A Hands-On Guided Tour