Generative AI and LLMs - From LlamaIndex to Enterprise Applications
Explore generative AI, LLMs, and RAG with Jerry Liu, CEO of LlamaIndex. Gain insights on enterprise adoption, entrepreneurship, and the societal impact of AI in this comprehensive discussion on cutting-edge technologies.
37% Better Output with 15 Lines of Code - Llama 3 Improved RAG
Improve RAG performance for Llama 3 models with simple code modifications, enhancing output quality by 37% for both local 8B and cloud-based 70B versions.
Generative AI for Data Platforms - Cutting to the Chase
Explore AI-infused lakehouses: integrating generative AI with data platforms for enhanced code generation, natural language queries, and semantic search. Includes live demo of IoT event processing.
Unleashing the GenAI Magic - Building a Chatbot for Traders with Generative AI
Explore the creation of a trader-focused chatbot using Generative AI, covering prompt engineering, Retrieval Augmented Generation, and unconventional tech choices for robust, scalable solutions.
Taylor Swift and the Democratization of AI for Developers - React Miami 2024
Explore how AI tools like OpenAI and Vercel's AI SDK enable developers to create smart experiences, illustrated through the development of SwiftieGPT, a Taylor Swift-focused chatbot.
A Builder's Guide to Generative AI - Getting Started with AWS Bedrock
Explore Generative AI with AWS expert Du'An Lightfoot, covering basics, RAGs, LLMs, and a live AWS Bedrock demo. Gain insights into leveraging AI models and agents for innovative tech projects.
LangChain Multi-Query Retriever for RAG - Advanced Technique for Broader Vector Space Search
Learn advanced RAG techniques using LangChain's Multi-Query Retriever. Explore how to broaden search results, customize queries, and integrate this method into larger RAG pipelines for improved AI applications.
Leveraging LLMs for Enterprise Data Management - Strategies and Applications
Explore essential LLM strategies for non-public data applications, including prompt engineering, fine-tuning, and Retrieval Augmented Generation. Learn to transform enterprise data processes with practical techniques and real-world examples.
Getting Started with Retrieval Augmented Generation (RAG) in Java and Spring AI
Learn to implement Retrieval Augmented Generation (RAG) using Java and Spring AI. Explore RAG's applications, setup process, and practical implementation with a simple example.
Private RAG with Open Source and Custom LLMs - BentoML and OpenLLM
Explore practical considerations for building private RAG applications using open source and custom LLMs. Learn about OpenLLM and optimize inference performance for RAG deployments.
Orchestrating RAG: Retrieval, Canopy, and Pinecone
Explore orchestrating RAG pipelines, optimizing retrieval strategies, and leveraging Canopy and Pinecone for scalable AI applications with billions of documents.
VertexAI Embeddings with Pinecone and LangChain - Building a Retrieval Component
Aprenda a implementar un componente de recuperación utilizando VertexAI Embeddings, Pinecone y Langchain para aplicaciones de aprendizaje automático y ciencia de datos.
Prompt Engineering Techniques for Microsoft Phi3-mini 128k Instruct
Explore prompt engineering techniques for Microsoft Phi3-mini 128k Instruct, including zero-shot, few-shot, CoT, and RAG approaches.
From RAG to RICHES - Retrieval Interlaced with Sequence Generation
Innovative AI approach integrating retrieval into sequence generation, simplifying RAG systems. Adapts to new tasks through prompting, supports multi-hop retrievals, and excels in Open Domain Question Answering.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding
Explore G-Retriever: a novel approach for textual graph understanding, enabling conversational interaction with graphs and enhancing AI applications in drug discovery and beyond.
Navigating Challenges and Enhancing Performance of LLM-based Applications
Explore strategies for optimizing Retrieval-Augmented Generation applications, addressing challenges in evaluation and performance enhancement for improved accuracy and contextual depth in AI-generated responses.
Retraining vs RAG vs Context - Using Local Data on Large Language Models
Explore techniques to enhance LLM functionality using retraining, RAG, and context documents for local and online models.
Building a RAG Application with GitHub Models and Postgres from Scratch
Learn to build a Retrieval Augmented Generation (RAG) application from scratch using GitHub Models and PostgreSQL, enhancing AI model capabilities for smarter applications.
Beyond Vectors: Evolving GenAI through Transformative Tools and Methods
Explore GenAI evolution beyond Vector Databases. Master data quality for RAG, navigate structural context, and leverage strategic logging for enhanced AI applications and business insights.
Private AI Implementation and Use Cases at VMware
Discover how VMware leverages private AI services internally, from coding assistance to document search using RAG, exploring their implementation of LLMs and cloud-smart approach to enterprise AI solutions.
VMware Private AI Foundation with NVIDIA - Enterprise Generative AI Platform Overview
Discover how to deploy and manage enterprise-level generative AI applications with VMware's integrated solution, featuring NVIDIA NeMo for customization, enhanced security, and seamless data privacy controls.