Retrieval-Augmented Generation
The discussion highlights the importance of citations in language models, allowing users to trace the origins of information and avoid the pitfalls of anthropomorphization. By integrating retrieval-augmented generation, models become more reliable and usable, enabling companies to leverage their internal documentation for accurate responses rather than fabricated content. This shift significantly enhances the way applications are built and utilized in production settings.In this clip
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Machine Learning Street Talk (MLST)
Cohere co-founder Nick Frosst on building LLM apps for business
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 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 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 and the clip Model Evaluation Insights?