cover of episode Need More Relevant LLM Responses? Address These Retrieval Augmented Generation Challenges

Need More Relevant LLM Responses? Address These Retrieval Augmented Generation Challenges

2024/1/5
logo of podcast Machine Learning Tech Brief By HackerNoon

Machine Learning Tech Brief By HackerNoon

Frequently requested episodes will be transcribed first

Shownotes Transcript

This story was originally published on HackerNoon at: https://hackernoon.com/need-more-relevant-llm-responses-address-these-retrieval-augmented-generation-challenges-part-1). we look at how suboptimal embedding models, inefficient chunking strategies and a lack of metadata filtering can make it hard to get relevant responses from you Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning). You can also check exclusive content about #retrieval-augmented-generation), #vector-search), #vector-database), #llms), #embedding-models), #ada-v2), #jina-v2), #good-company), and more.

        This story was written by: [@datastax](https://hackernoon.com/u/datastax)). Learn more about this writer by checking [@datastax's](https://hackernoon.com/about/datastax)) about page,
        and for more stories, please visit [hackernoon.com](https://hackernoon.com)).
        
            
            
            we look at how suboptimal embedding models, inefficient chunking strategies and a lack of metadata filtering can make it hard to get relevant responses from your LLM. Here’s how to surmount these challenges.