RAG vs Fine Tuning vs Prompt Engineering: Use Cases And Key Differences Explained | Simplilearn

🔥Applied Generative AI Specialization, delivered by Simplilearn in collaboration with Purdue University - https://www.simplilearn.com/applied-ai-course?utm_campaign=2m1rWVy4uqE&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Partnership is with E&ICT of IIT Kanpur - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning? In this tutorial we are going to learn about rag vs fine tuning vs prompt engineering. RAG is when an AI model connects with external data like PDFs, websites, databases, or company documents to give updated and accurate answers. Fine-tuning is when an existing AI model is trained again with specific data so it can follow a particular tone, style, format, or domain. Prompt engineering is the process of writing clear and specific instructions so the AI model gives better and more useful responses. 00:00 Introduction to Prompt Engineering, RAG, and Fine-Tuning 02:15 What is RAG? 10:15 What is Fine-Tuning? 16:15 What is Prompt Engineering? 18:12 Key differences between all three 17:35 Real-world examples for each 21:02 Sample project structures ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out More AI Videos By Simplilearn: https://youtube.com/playlist?list=PLEiEAq2VkUULyr_ftxpHB6DumOq1Zz2hq #ragvsfinetuningvspromptengineering #ragexplained #finetuningexplained #promptengineeringtutorial #ragvsfinetuningdifferences #promptengineeringvsfinetuning #finetuningllmtutorial #simplilearn #2026 ➡️ About Applied Generative AI Specialization This Applied Generative AI course equips you to develop AI-powered applications using industry-relevant tools and frameworks. Build expertise in key concepts like prompt engineering, GANs, VAEs, and LLM architectures while exploring advanced topics, including agentic AI, LLM fine-tuning, RAG, and AI governance for practical, real-world deployments.