Back to Basics: Understanding Retrieval Augmented Generation (RAG)
As interest in Large Language Models (LLMs) grows, numerous developers and organizations are hard at work creating programs that take use of their potential....
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About this video
As interest in Large Language Models (LLMs) grows, numerous developers and organizations are hard at work creating programs that take use of their potential. However, the topic of how to enhance the performance of the LLM application arises when the pre-trained LLMs do not function as anticipated or hoped for out of the box. At this stage model fine tuning or retrieval-augmented generation (RAG) to enhance the outcomes is needed. In this episode, join Nitin as he walks through what RAG is and best practices for implementation using AWS with Bedrock/FM and AWS services.
Additional Resources:
Amazon Bedrock: https://aws.amazon.com/bedrock/
What is RAG?: https://aws.amazon.com/what-is/retrieval-augmented-generation/
What are vector databases?: https://aws.amazon.com/what-is/vector-databases/
Knowledge Bases for Amazon Bedrock: https://aws.amazon.com/bedrock/knowledge-bases/
Agents for Amazon Bedrock: https://aws.amazon.com/bedrock/agents/
Amazon Titan Text Embeddings models: https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html
Amazon Titan Multimodal Embeddings model: https://docs.aws.amazon.com/bedrock/latest/userguide/titan-multiemb-models.html
Check out more resources for architecting in the #AWS cloud:
http://amzn.to/3qXIsWN
#AWS #AmazonWebServices #CloudComputing #BackToBasics #GenerativeAI #AmazonBedrock
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Published
Jan 26, 2024
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hd
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#AWS #Amazon Web Services #Cloud #AWS Cloud #Cloud Computing #Amazon AWS #Amazon Bedrock #Vector embedding #Retrieval Augmented Generation #Large Language Models #RAG #Foundational Models #Generative AI #Artificial Intelligence #retrieval-augmented generation
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