Retrieval Augmented Generation (RAG) vs In-Context-Learning (ICL) vs Fine-Tuning LLMs

#ai #rag #llm #prompt
This video is a simplified explanation of Retrieval Augmented Generation (RAG) vs In-Context-Learning (ICL) vs Fine-Tuning LLMs for beginners; three terms and concepts related to the ways to use Large Language Models and increase the accuracy of their responses. I have already explained these in my previous videos separately so I thought I would give you all in one place.

Here are some relevant hands-on code videos:
Easiest way to Fine-tune LLMs Locally + Code - NO GPU Needed:
   • ✅ Easiest Way to Fine-tune LLMs Local...  

Mixture of Agents (MoA):    • 🔴 Mixture of Agents (MoA) Method Expl...  
AI Agents With CrewAI And Ollama:    • 💯 FREE Local LLM - AI Agents With Cre...  

Learn more about the main AI concepts here:
https://github.com/Maryam-Nasseri/AI-...

Key terms and concepts used in the video:
AI agents, RAG, GPT-4o, Gemini 1.5 Pro, ICL, fine-tuning, large language models, zero-shot, few-shot learning, many-shot learning, context, context window, multimodal models, generative AI, LLM evaluation, image classification, natural language processing

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