On #AskMoreOfAI, we discuss how Retrieval Augmented Generation (RAG) improves your generative AI results with structured and unstructured data sources, like SQL databases and PDFs, and how LLM fine-tuning enhances response accuracy. Hear the conversation with the CEOs of @Perplexity_AI, @Llama_Index, and @LangChainAI here on X, YouTube, and your favorite podcast platforms. Episode #15: Workflows & Tooling to Create Trusted AI Timestamps: 0:00 - Introduction 0:50 - Perplexity AI overview with @AravSrinivas 4:23 - LangChain overview with @hwchase17 6:23 - LIamaIndex overview with @jerryjliu0 7:55 - When do you use RAG versus fine-tuning? 9:22 - Aravind's background in artificial intelligence 11:40 - How do you manage ever-changing models with your tools? 13:40 - Value of open source projects 14:44 - Will any of you build your models in the future? 16:45 - Advantages of post-training ‌models 18:18 - Disadvantages of post-training 19:49 - How Perplexity AI tracks evals 20:30 - Thoughts ...