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.
AI Agent - Open-Source Perplexity Alternative Using LangGraph and Advanced Prompt Engineering
Explore Jar3d, an open-source AI agent using LangGraph and advanced prompt engineering. Learn to build a Perplexity alternative with Llama 3.1 for research-intensive tasks and local operation.
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.
RAG with Llama 3.1 for Google Trends Data Scraping and Summarization - Streamlit Web App
Discover how to build a RAG pipeline using LLaMA 3.1 to automate article discovery and summarization based on Google Trends, showcased in a Streamlit web app for efficient content curation.
Introduction to Retrieval-Augmented Generation (RAG) - Lecture 6.1
Explore Retrieval-Augmented Generation (RAG) for precise document-based Q&A using LLMs and LangChain. Learn about retrieval, augmentation, and generation phases to enhance model accuracy.
Claude Prompt Caching: A Potential Alternative to RAG for AI Systems
Explore Claude's prompt caching as a potential RAG alternative, examining its functionality and effectiveness through practical examples and comparisons.
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.
RAG from Scratch with Llama 3.1 - Building a Custom Data Chatbot
Hands-on tutorial for building a RAG system from scratch using Llama 3.1, Groq API, Sqlite-vec, and FastEmbed. Learn to create a chatbot with custom data without external libraries.
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.
Databricks Vector Search - What, Why and How
Explore Databricks Vector Search for enhancing GenAI applications with semantic search capabilities, addressing challenges in scalability, accuracy, and production deployment.
Building a Production Scale, Totally Private, OSS RAG Pipeline with DBRX, Spark, and LanceDB
Discover how to create a secure, scalable AI pipeline using open-source tools DBRX, Spark, and LanceDB. Learn to implement private generative AI solutions for enterprises without compromising data security or performance.
Vector Search Over Complex Semi-Structured Data
Explore production-grade vector search systems combining unstructured and structured data, with focus on user objectives and feedback loops for improved results in various applications.
Scaling Marketing and Docs with a Privacy-Safe RAG Model
Unlock unstructured data's value using LLMs while ensuring privacy. Learn how Skyflow developed a privacy-safe RAG model for marketing and documentation, leveraging Databricks, Streamlit, and open-source tools.
Introduction to Retrieval Augmented Generation (RAG) and Implementation with Databricks
Discover RAG's potential to enhance AI applications with external knowledge sources. Learn implementation steps using Databricks for more intelligent, context-aware AI solutions.
AutoGen RAG - Creating AI Agents with a Second Brain
Explore how to enhance AI agents with Retrieval-Augmented Generation, enabling more accurate and context-aware responses through a "second brain" approach. Learn implementation steps and practical examples.
Chunking Strategies in RAG - Optimizing Data for Advanced AI Responses
Explore advanced chunking strategies for RAG applications, including character, recursive, document-based, semantic, and agentic methods to optimize AI responses and improve data processing efficiency.
RAG from the Ground Up with Python and Ollama - Building a Document Interaction System
Build a RAG system with Python and Ollama: parse documents, create embeddings, implement relevance scoring, and enable LLM interaction with your own datasets.
RAG - Extracting Information from PDFs Using LlamaIndex, Gemini, and Nvidia NIM with Llama 3.2
Aprende a extraer información de PDFs utilizando LlamaIndex, implementando modelos como Gemini y Nvidia NIM para embeddings junto con LLama 3.2 para procesamiento de lenguaje natural.
Building Arcane - A Retrieval-Augmented Generation System for Investment Policies
Discover how to build Arcane, a RAG system that revolutionizes policy retrieval in financial institutions, boosting productivity through AI-powered document search and information extraction.
Multimodal RAG with LangChain, VertexAI, Gemini Pro and Chroma
Aprende a construir aplicaciones RAG multimodales utilizando LangChain, VertexAI y Chroma para procesar eficientemente datos no estructurados como PDFs e imágenes.
Multimodal RAG with LlamaIndex, Google AI, Gemini Pro and Qdrant
Explore cómo crear aplicaciones RAG multimodales utilizando LlamaIndex, Google AI Studio SDK y Gemini Pro para extraer información estructurada de imágenes con Qdrant como vector store.