Retrieval-Augmented Generation (RAG) vs LLM Fine-Tuning, by Cobus Greyling

RAG is known for improving accuracy via in-context learning and is very affective where context is important. RAG is easier to implement and often serves as a first foray into implementing LLMs due…

How to improve RAG results in your LLM apps: from basics to advanced, by Guodong (Troy) Zhao

Retrieval Augmented Generation at Planet Scale

A Practitioners Guide to Retrieval Augmented Generation (RAG), by Cameron R. Wolfe, Ph.D., Mar, 2024

A New Study Compares RAG & Fine-Tuning For Knowledge Base Use-Cases

Enhancing LLMs with Retrieval Augmented Generation

T-RAG = RAG + Fine-Tuning + Entity Detection

RAG Vs Fine-Tuning Vs Both: A Guide For Optimizing LLM Performance - Galileo

Steps In Evaluating Retrieval Augmented Generation (RAG) Pipelines, by Cobus Greyling

Revolutionizing AI Conversations: Unleashing the Dynamic Power of RAG — Retrieval-Augmented Generation, by Hira Ahmad

Fine-tuning an LLM vs. RAG: What's Best for Your Corporate Chatbot?