Retrieval Augmented Generation (RAG) with Data Streaming for GenAI Applications
Explore Retrieval Augmented Generation (RAG) with data streaming to enhance LLM outputs. Learn how Apache Kafka and Flink enable real-time, contextualized data for reliable GenAI applications.
Understanding Generative AI: RAG, Semantic Search, Embeddings, and Vectors
Explore key Generative AI concepts like RAG, semantic search, embeddings, and vectors. Understand their importance in leveraging AI with your data for improved information retrieval and processing.
Introduction to QA Bots with RAGs and LLMs
Explore QA systems using LLMs and RAGs, learning to create powerful question-answering bots on custom datasets.
Unlocking Real-World LLM Use Cases - Techniques for Accurate Results
Explore real-world LLM applications, overcome limitations, and leverage techniques like RAG to ground results. Learn to bridge LLMs with enterprise Gen AI apps using vector-supported databases.
Building a Retrieval Augmented Generation System: Challenges and Best Practices
Explore the challenges and best practices in building a Retrieval Augmented Generation system, focusing on data quality, query engines, and contextualization for informed decision-making in your projects.
RAG and RecSys - Parallels in Retrieval Quality Improvement
Explore parallels between RAG and recommender systems, focusing on improving retrieval quality through hybrid reranking techniques using LanceDB in this concise AI production talk.
Retrieval Augmented Generation (RAG) for LLM-Powered Apps
Explore retrieval augmented generation (RAG) for enhancing LLM-powered applications, gaining insights into its importance and practical implementation.
Enterprise Jargon and RAG Systems - Effective Tactics for Improved Performance
Discover effective tactics to enhance RAG and retrieval systems for handling enterprise jargon, improving LLM-powered tools and applications for businesses.
Why Retrieval Augmented Generation (RAG) is Overrated
Explore the limitations of Retrieval Augmented Generation (RAG), including hallucinations, complexity, and cost. Learn practical approaches to make RAG more effective in AI applications.
Practical Data Considerations for Building Production-Ready LLM Applications
Discover practical tips for managing data in scalable LLM systems. Learn how LlamaIndex and Ray provide tools for building robust, production-ready AI applications using private data.
Developing and Serving RAG-Based LLM Applications in Production
Learn to develop, evaluate, and scale RAG-based LLM applications for production, including advanced topics like hybrid routing to bridge the gap between open-source and closed LLMs.
Question Answering over Documents with RAG - Lecture 6.4
Explore Retrieval-Augmented Generation (RAG) to enhance LLMs with external data, improving accuracy and relevance in corporate settings. Learn to set up and query a RAG system using sample employee data.
Lies, Damned Lies, and Large Language Models - Measuring and Reducing Hallucinations
Explore LLM hallucinations, measure misinformation using TruthfulQA dataset, and learn about retrieval augmented generation to enhance model reliability and accuracy.
A Python Developer's Guide to AI in 2024
Comprehensive guide for Python developers on AI trends in 2024, covering machine learning, neural networks, computer vision, LLMs, RAG, and AI agents.
Retrieval-Augmented Generation: Enhancing AI Models - Lecture 7
Explore Retrieval-Augmented Generation (RAG): theory, components, and practical implementation. Enhance AI models' capabilities with this comprehensive overview for beginners and enthusiasts alike.
Build a Q&A App with RAG Using Gemini Pro and LangChain
Learn to create an intelligent Q&A app using Retrieval-Augmented Generation, Gemini Pro, and LangChain. Master advanced retrieval techniques to enhance chatbot performance and build a powerful AI-driven application.
Building a Q&A App with RAG, LangChain, and Open-Source LLMs - Step-by-Step Guide
Step-by-step guide to building a Q&A application using RAG, LangChain, and open-source LLMs. Learn to create a sophisticated system leveraging cutting-edge AI technology for efficient question answering.
Building Multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus - LLM App Development
Learn to build a multimodal AI RAG application using LlamaIndex, NVIDIA NIM, and Milvus. Covers document processing, vector database integration, inference with Llama 3, and seamless orchestration for efficient Q&A experiences.
LLM News: GPT-4o Mini, Codestral Mamba, Prompt Engineering Methods, and More - January 2024 Update
Explore latest AI advancements: GPT-4o mini, Mistral Nemo, Codestral Mamba, prompt engineering techniques, long-context reasoning, efficient RAG, spreadsheet LLM, and more. Stay updated on cutting-edge machine learning developments.
Building the World's Largest RAG for Knowledge Management at CVS Health
Explore CVS Health's innovative approach to building a large-scale RAG system for unified knowledge management, addressing challenges and strategies for implementing enterprise-wide information access.
Unleashing the Potential of Unstructured Data with LLMs and Databricks
Explore how the North Dakota University System leverages LLMs and Databricks to extract insights from unstructured data, creating an AI portal for staff and students.