Workshop: Build RAGs as AI Product Managers - AI PM Community Session #50 

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🎯 Key Takeaways for quick navigation: 00:00 *📝 Introduction to Retrieval-Augmented Generation (RAG)* - Brief introduction to the workshop and RAG. - Explains the motivation for using RAG due to limitations of LLMs. - Overview of how RAG combines LLMs with contextual information to improve responses. 01:06 *🏗️ Architecture of RAG System* - Explanation of RAG system architecture. - Embeddings are stored in a vector database and queried for responses. - Example use case: Financial analysis on SEC 10K forms. 03:44 *📂 Setting Up the RAG System* - Step-by-step setup of the RAG system for the workshop. - Installation of necessary libraries and dependencies. - Initial setup of vector database using ChromaDB and Llama Index. 05:50 *🔄 Chunking Strategy and Embedding Creation* - Simple chunking strategy using paragraph-based chunks. - Discussion on different chunking strategies and their trade-offs. - Initialization of embedding models and storing embeddings in the vector database. 09:38 *💬 Querying the RAG System* - Demonstration of querying the RAG system with financial questions. - Example questions related to Apple's financial health. - Handling queries and generating responses using the RAG setup. 12:01 *📊 Evaluating and Visualizing Results* - Discussion on evaluation metrics for RAG models. - Mention of popular libraries for evaluation. - Brief talk on the visualization of results and the complexity involved. 27:49 *☁️ Comparing Cloud and Local Deployment for LLMs* - Discussion on the differences between running LLMs locally versus on the cloud. - Cloud solutions handle infrastructure and scaling. - Local deployments require managing LLMOps and infrastructure. 28:31 *🔄 Automating Model Testing* - Approaches to automate the testing of model responses. - Using benchmark datasets to evaluate model performance. - Challenges in ensuring consistent confidence in model responses. 30:24 *🔧 Controlling Model Output* - Techniques to control the quality of model outputs. - Adjusting temperature settings to manage response confidence. - Trade-offs between performance and accuracy. 33:22 *🏆 Evaluation Metrics for RAG Models* - Evaluation metrics used to measure RAG model performance. - Libraries like RAGAS provide standard metrics for evaluation. - Importance of testing on diverse datasets for reliable metrics. 37:05 *🔍 Using Existing Vector Databases* - Leveraging existing vector databases for RAG implementation. - Options for integrating RAG with current vector databases. - Benefits of using cloud-based vector databases for scalability. 40:24 *🛡️ Ensuring Data Security* - Strategies to ensure data security when using RAG. - Anonymizing sensitive information before processing. - Using on-premise solutions to maintain data privacy. 44:57 *🏢 Companies Implementing RAG* - Examples of companies effectively implementing RAG. - Mention of Deep Judge in the legal space. - Importance of exploring successful RAG implementations for inspiration. Made with HARPA AI

great content, can you improve the sound somehow?!