Practical AI RAG continues to rise
53 snips
Apr 10, 2024 
Demetrios Brinkmann, known as “the funniest guy in AI,” shares insights from the MLOps Community's latest survey. The conversation dives into the rise of Retrieval-Augmented Generation (RAG) and its advantages over traditional methods. They explore how RAG engages non-technical users and tackle the challenges of AI implementation, particularly with chatbots. With a nod to the future, the topic of neuromorphic computing sparks intrigue as they discuss potential advancements that could reshape AI technology.
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RAG vs. Fine-Tuning
- Fine-tuning's popularity has decreased, while Retrieval Augmented Generation (RAG) is now the go-to approach.
- Generative AI models excel as assistants and automators, not necessarily as predictors or analytics tools.
Distinct Workloads
- Traditional ML workloads like fraud detection and recommender systems will likely remain distinct from generative AI use cases.
- Generative AI models are best suited for tasks such as transcription, code generation, and acting as copilots.
AI Budgets and Use Cases
- Budgets are increasingly allocated to AI, with many companies using new budgets for exploration.
- ML engineers focus on identifying high-impact use cases and justifying them to their organizations.
