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 finetuning 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
