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…
Knowledge Graphs & LLMs: Fine-Tuning vs. Retrieval-Augmented Generation - Graph Database & Analytics
Understanding RAG and fine-tuning of LLMs, by Ashok Poudel
Fine Tuning or Retrieval Augmented Generation (RAG), That Is the Question, by Peng Liu, Mar, 2024
Jugal Oza on LinkedIn: Introduction to Kalman Filter
Prompt Engineering: Retrieval Augmented Generation - DZone
Augmenting LLMs: Fine-Tuning or RAG? - by Damien Benveniste
NEFTune”: Discover How Noisy Embeddings Act as Catalyst to Improve Instruction Finetuning!, by AI TutorMaster
Prompt Editing Based On User Feedback, by Cobus Greyling
Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs
A Gentle Introduction to Retrieval Augmented Generation (RAG)
