Fine-Tuning LLMs With Retrieval Augmented Generation (RAG), by Cobus Greyling

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…

Evaluating RAG Applications with Trulens, by zhaozhiming

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

Retrieval Augmented Generation (RAG) Safeguards Against LLM Hallucination

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

Hopsworks Solution - Fine-Tuning LLMs & RAG for GenAI

Leveraging LLMs on your domain-specific knowledge base

Evaluating RAG Metrics Across Different Retrieval Methods, by Harpreet Sahota, Feb, 2024

Tuning the RAG Symphony: A guide to evaluating LLMs, by Sebastian Wehkamp, Feb, 2024

Advanced RAG 01: Problems of Naive RAG

Leveraging LLMs on your domain-specific knowledge base

Navigating the AI Hype and Thinking about Niche LLM Applications, by Hadi Javeed

RAG Vs Fine tuning Vs Both. Introduction, by Ramprasath S

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

Retrieval augmented generation: Keeping LLMs relevant and current