AI Application Complexity
Building AI applications in enterprises presents unique complexities compared to traditional software. The integration of retrieval augmented generation enhances the capabilities of models by leveraging valuable data from various sources, transforming how organizations utilize their hidden data. This approach allows for a more dynamic and effective use of machine learning, showcasing the importance of ongoing data interaction.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Cohere's SVP Technology - Saurabh Baji
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 Building LLM-Based Applications with Azure OpenAI with Jay Emery - 657 and the episode with Cohere co-founder Nick Frosst on building LLM apps for business?
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?