Scaling AI Solutions
Companies face significant challenges when scaling AI solutions, particularly in selecting the right models and determining whether to build in-house applications or use ready-made options. Many organizations prefer to focus on their documents and desired outcomes rather than delving into the complexities of vector databases. Additionally, the emergence of reasoning and action frameworks allows companies to leverage large language models to invoke APIs, enhancing their capabilities in data retrieval and decision-making.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
The Enterprise LLM Landscape with Atul Deo - 640
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?
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 Vectoring in on Pinecone 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?