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In this comprehensive Simplilearn video, we dive into key AI tuning techniques, starting with RAG (Retrieval-Augmented Generation), which enhances pre-trained language models by integrating external data retrieval to improve response accuracy. We then explore Fine-Tuning, where pre-trained models are adjusted using specific datasets to optimize performance for targeted tasks. Next, we cover Prompt Tuning, a more lightweight approach that focuses on refining input prompts rather than retraining models. Throughout the video, we compare these methods based on their approach, purpose, use cases, and data dependency, while also highlighting their similarities, such as their shared goal of optimizing AI outputs. Whether you're new to AI or an experienced professional, this video provides a detailed look into these essential techniques, helping you understand when and how to apply them effectively. Don't forget to like, share, and subscribe for more AI insights from Simplilearn!
-- Frequently Asked Questions
✅ Is prompt engineering in demand?
Prompt engineers are in demand in various industries, including finance, healthcare, tech industries, and more.
✅Is RAG just prompting?
RAG is a unique approach that combines the power of traditional language models with the precision of external knowledge bases. This method stands out for several reasons, making it particularly advantageous over solely prompting or fine-tuning in specific contexts
✅What is the difference between RAG and fine tuning?
Although RAG excels in generating up-to-date responses and minimizing the risk of hallucinations, its accuracy may vary based on the domain or task at hand. On the other hand, fine-tuning focuses on enhancing a model's domain-specific understanding, which often leads to more accurate responses and predictions.
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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
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