Learn how to implement Retrieval Augmented Generation (RAG) using Cohere Command model via Amazon Bedrock & domain data in Elasticsearch.

Limitations of LLMs

Approaches to enhance LLMs

RAG using Elasticsearch and Cohere Command model through Amazon Bedrock

Why Cohere?

Why Elasticsearch?

Why Amazon Bedrock?

Solution overview

Step by step explanation

Conclusion

Generative AI is a type of Artificial Intelligence (AI) that can create new content and ideas, including conversations, stories, images, videos, and music. Like all AI, generative AI is powered by Machine Learning (ML) models—very large models that are pre-trained on vast corpora of data and commonly referred to as Foundation Models (FMs).

What is generative AI?

LLMs have certain limitations. One notable constraint is that they are trained on general domain corpora, making them less effective on domain-specific tasks. Also, LLMs are trained offline until a knowledge cutoff date. It will be agnostic of any developments that have happened after the knowledge cutoff date. This may lead to inaccurate interpretations.

What are the limitations of Large Language Models (LLMs)?

RAG using Elasticsearch & Cohere through Amazon Bedrock

Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch

Author

James Yi

Articles

Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch