RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdndTs Learn more about RAG vs. Fine-Tuning vs. Prompt Engineering here → https://ibm.biz/BdndTi How do AI chatbots deliver better responses? 🤔 Martin Keen explains RAG 🛠️, fine-tuning 🎯, and prompt engineering ✏️—methods that extend knowledge, refine responses, and build domain expertise. Learn how these strategies optimize large language models and improve AI outputs today! 🚀 AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdndTj #retrievalaugmentedgeneration #finetuning #promptengineering

▶︎
RAG vs Fine Tuning vs Prompt Engineering

▶︎
Generative AI in a Nutshell - how to survive and thrive in the age of AI

▶︎
Is RAG Still Needed? Choosing the Best Approach for LLMs

▶︎
RAG vs. CAG: Solving Knowledge Gaps in AI Models

▶︎
Most devs don't understand how LLM tokens work

▶︎
Prompt Engineering is dead.

▶︎
KV Cache: The Invisible Trick Behind Every LLM

▶︎
What is a Vector Database? Powering Semantic Search & AI Applications

▶︎
Fine-tuning Large Language Models (LLMs) | w/ Example Code

▶︎
MCP vs. RAG: How AI Agents & LLMs Connect to Data

▶︎
How AI agents & Claude skills work (Clearly Explained)

▶︎
What AI Agent Skills Are and How They Work

▶︎
Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

▶︎
What Are Large Reasoning Models (LRMs)? Smarter AI Beyond LLMs

▶︎
The 7 Skills You Need to Build AI Agents

▶︎
What is RAG ? | Completely Explained in 15 Minutes

▶︎
MCP vs API: Simplifying AI Agent Integration with External Data

▶︎
RAG vs Agentic AI: How LLMs Connect Data for Smarter AI

▶︎
Building Decision Agents with LLMs & Machine Learning Models

▶︎
