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Difference between Fine tuning and Retrieval Augmented Generation (RAG)
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10 Days of Gen AI: Day 7 Fine-Tuning vs. RAG: Which Approach is Right for Your LLM Project?
Large Language Models (LLMs) are transforming the AI landscape, but how do you optimize them for your specific needs? In this video, we'll break down the key differences between two powerful techniques: fine-tuning and Retrieval Augmented Generation (RAG).
What You'll Learn:
Fine-Tuning explained: Understand how fine-tuning customizes an LLM's internal knowledge for better performance on specific tasks.
RAG explained: Discover how RAG leverages external data sources to provide up-to-date and contextually relevant information during LLM generation.
Head-to-Head Comparison: We'll analyze the strengths and weaknesses of each approach, considering factors like cost, data requirements, and performance.
Use Case Scenarios: Explore real-world examples of when fine-tuning or RAG might be the ideal solution for your AI project.
Practical Tips: Get expert advice on choosing the right strategy and implementing it effectively.
Whether you're building a chatbot, a content generator, or any other LLM-powered application, this video will equip you with the knowledge to make informed decisions and achieve the best possible results.
Don't forget to like, subscribe, and hit the notification bell for more AI insights and tutorials!
#10DaysOfGenAI #LLM #AI #FineTuning #RAG #RetrievalAugmentedGeneration #MachineLearning #ArtificialIntelligence #NaturalLanguageProcessing #GPT #DataScience
Large Language Models (LLMs) are transforming the AI landscape, but how do you optimize them for your specific needs? In this video, we'll break down the key differences between two powerful techniques: fine-tuning and Retrieval Augmented Generation (RAG).
What You'll Learn:
Fine-Tuning explained: Understand how fine-tuning customizes an LLM's internal knowledge for better performance on specific tasks.
RAG explained: Discover how RAG leverages external data sources to provide up-to-date and contextually relevant information during LLM generation.
Head-to-Head Comparison: We'll analyze the strengths and weaknesses of each approach, considering factors like cost, data requirements, and performance.
Use Case Scenarios: Explore real-world examples of when fine-tuning or RAG might be the ideal solution for your AI project.
Practical Tips: Get expert advice on choosing the right strategy and implementing it effectively.
Whether you're building a chatbot, a content generator, or any other LLM-powered application, this video will equip you with the knowledge to make informed decisions and achieve the best possible results.
Don't forget to like, subscribe, and hit the notification bell for more AI insights and tutorials!
#10DaysOfGenAI #LLM #AI #FineTuning #RAG #RetrievalAugmentedGeneration #MachineLearning #ArtificialIntelligence #NaturalLanguageProcessing #GPT #DataScience
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