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

$ 27.50

4.7 (233) In stock

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…

Enhancing LLMs with Retrieval-Augmented Generation

Fine-Tuning for Reasoning and Context — The Key to Better RAG Systems, by Anthony Alcaraz, Feb, 2024

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

RAPTOR: Supercharge your RAG with Deeper Context Understanding, by AI TutorMaster, Mar, 2024

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

12 Prompt Engineering Techniques, PDF, Cognitive Science

Fine-Tuning OpenAI model for Specialized Tasks, by Luc Nguyen

Which is better, retrieval augmentation (RAG) or fine-tuning? Both.

RAG vs. Fine-tuning: Here's the Detailed Comparison, by Amit Yadav

A Gentle Introduction to Retrieval Augmented Generation (RAG)

Rethinking Embedding-based Retrieval-Augmented Generation (RAG) for Semantic Search and Large Language Models (LLMs), by Aivin Solatorio

RAGs from scratch — Why & What?!!, by Arion Das, Feb, 2024

Cobus Greyling on LinkedIn: Retrieval Augmented Generation (RAG) Safeguards Against LLM Hallucination

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

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