
Explore top retrieval models for RAG: OpenAI's text-embedding-ada-002, Cohere's Embed v3, and e5-base-v2. Compare performance, inference speeds, and results across various tests to enhance your AI applications.

Explore building a GPT-powered sales agent using JSON mode, with and without RAG, to recommend products based on user preferences through natural conversation.

Explore integrating AI capabilities into your applications using Weaviate and Docker. Learn about vector databases, LLMs, and overcoming hallucination through Retrieval Augmented Generation for AI-powered features.

Build a Star Wars AI RAGbot in just 2 hours! Learn to create an interactive chatbot using AI and retrieval-augmented generation techniques, bringing the galaxy far, far away to life.

Step-by-step guide to set up a retrieval-augmented generation (RAG) system using NVIDIA AI Workbench, enabling developers to create AI projects on their local machines without a GPU in about an hour.

Exploring AI's evolution, LLMs, and AGI with insights on enterprise adoption, vector databases, and the interplay of art and technology. Delves into key concepts shaping AI's future trajectory.

Explore latest AI advancements: Moshi, RAG practices, State of AI Report, Million Tiny Experts, GPT4All, and RouteLLM. Gain insights into cutting-edge developments shaping the future of artificial intelligence.

Explore deploying Mixtral 8X7B, an impressive open-source LLM outperforming GPT-3.5. Learn setup, prompt formatting, agent capabilities, and integration with RAG for enhanced AI applications.

Explore vector search, retrieval-augmented generation, and scaling challenges. Learn from benchmarks, AI pre-training experiments, and serving billions of vectors. Discover optimal GPU choices and efficient search result serving methods.

Explore the future of Generative AI technology, including autonomous agents, human-like memory integration, and multi-model systems in this insightful talk on emerging AI stacks.

Explore AI data management innovation with LlamaIndex CEO Jerry Liu, covering integration of diverse data formats, retrieval techniques, and the future of AI-driven systems in this insightful discussion.

Explore retrieval-augmented generation and learn to customize AI models with your own data, enhancing their ability to answer unique questions about your business or projects.

Learn to create an AI agent with RAG capabilities, utilizing various tools for task-specific actions. Explore data querying, agent building, and unstructured data handling in Python.

Explore OCI Generative AI Service's mechanics, including flexible LLM fine-tuning and improved GPU cluster management for simplified, cost-effective AI application integration.

Learn to create efficient chatbots using LangChain Framework. Explore LLM fundamentals, effective prompting, and the RAG approach to enhance automation and simplify complex processes.

Optimizing GenAI and RAG deployment on multi-cluster Kubernetes: resource-aware scheduling for efficient, scalable, and cost-effective production environments across geographies.

Learn to build an advanced RAG system using LangChain, Llama 3, and open models. Parse PDFs, create embeddings, use vector databases, and implement reranking for intelligent question-answering.

Learn to evaluate AI agents using RAGAS, covering metrics like context recall, precision, faithfulness, and answer relevancy for improved RAG pipeline performance.

Learn to build an AI app using RAG and Langflow without coding. Create a chatbot, integrate OpenAI, use vector databases, and implement retrieval-augmented generation in minutes.

Learn to build and evaluate RAG applications using AIConfig in VS Code. Transform your editor into a prompt IDE for efficient AI development, testing, and optimization.

Explore open-source multi-modal RAG system using ChromaDB and OpenCLIP embeddings. Learn implementation, code review, and get a preview of advanced coding techniques for AI-powered information retrieval.