Grounded Generation Explained
Amr discusses the innovative concept of grounded generation, emphasizing its ability to utilize a robust retrieval engine to find relevant facts without bias. He highlights the importance of balancing semantic and lexical matching in data processing, which minimizes issues like hallucination and copyright infringement. Additionally, he notes the appeal of tools like Langchain for developers seeking flexibility, despite the inherent complexities of AI development.In this clip
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Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?