Real-World AI Challenges
Real-world AI applications face unique challenges, particularly regarding the use of sensitive data like electronic medical records and financial statements. Fine-tuning models with such data is not feasible due to privacy concerns, leading to a focus on retrieval-augmented generation (RAG) methods. The conversation highlights the need for frameworks that effectively manage and retrieve live, dynamic data while bridging the gap between AI research and practical applications.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Are Vector DBs the Future Data Platform for AI? with Ed Anuff - 664
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 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?