All about RAG!

    Retrieval. Augmented. Generation!

    Vectorize

    Collection thumbnail

    Recently added

    These RAG pipelines will change the way you build AI applications
    Lesson 7

    These RAG pipelines will change the way you build AI applications

    This lesson demonstrates building a Retrieval Augmented Generation (RAG) pipeline using the Vectorize platform to quickly process and query large PDF datasets, such as Denver's zoning regulations. The tutorial covers key steps including data ingestion from Dropbox, configuring extraction strategies, selecting a vector database (like Pinecone), and deploying a functional chatbot for efficient question answering.

    19mMar 3, 2025
    Free
    How To Make RAG Models Perform Like A Pro
    Lesson 6

    How To Make RAG Models Perform Like A Pro

    Master efficient document searching with vectorize.io's RAG pipeline builder; optimize your search by testing various embedding models and chunking strategies to achieve optimal performance using metrics like NDCG and Recall.

    4mMar 3, 2025
    Free
    Build a Slack AI assistant with n8n and Vectorize (that actually works!)
    Lesson 5

    Build a Slack AI assistant with n8n and Vectorize (that actually works!)

    This lesson shows how to build a Slack bot that uses Vectorize's RAG pipeline and OpenAI to answer questions. The process involves setting up a Slack app, configuring an n8n workflow to connect to Vectorize and OpenAI, and optimizing prompt engineering for accurate responses.

    25mMar 3, 2025
    Free
    Stop Making These 5 Mistakes With Your RAG Applications!
    Lesson 4

    Stop Making These 5 Mistakes With Your RAG Applications!

    Master Retrieval Augmented Generation (RAG) by leveraging vector databases for efficient similarity searches within unstructured data. Optimize your RAG system through strategic chunking, meticulous metadata design, and continuous data updates to ensure accuracy and real-time relevance.

    9mFeb 8, 2025
    Free
    RAG Chatbot Tutorial using Vectorize, Elasticsearch, and Vercel
    Lesson 3

    RAG Chatbot Tutorial using Vectorize, Elasticsearch, and Vercel

    This lesson demonstrates building and deploying a Retrieval-Augmented Generation (RAG) chatbot using Vectorize, integrating LLMs like OpenAI and vector databases for efficient information retrieval. The process covers pipeline creation, data ingestion, chatbot integration, and deployment on platforms like Vercel, emphasizing ease of use and scalability.

    20mFeb 8, 2025
    Free

    All lessons

    Build RAG apps fast over any website
    Lesson 1

    Build RAG apps fast over any website

    This lesson teaches you to build a Retrieval Augmented Generation (RAG) pipeline using Vectorize and Qdrant. It covers web crawling, vector database integration, and the configuration of a conversational AI system for efficient information retrieval from online sources.

    15mFeb 8, 2025
    Free
    Building an AI Agent from Scratch
    Lesson 1

    Building an AI Agent from Scratch

    This lesson explores the architecture of an AI-powered technical support agent, detailing its interaction with LLMs and various tools via function calls to automate responses and escalate complex issues to human agents. The system leverages OpenAI's API, incorporating features like persistent storage, error handling, and a chain-of-thought prompting approach for improved accuracy and efficiency.

    9mFeb 8, 2025
    Free
    Creating Retrieval Agents with OpenAI Swarm
    Lesson 2

    Creating Retrieval Agents with OpenAI Swarm

    This lesson teaches you to build a Retrieval Augmented Generation (RAG) pipeline using Vectorize, integrating it with Swarm for multi-agent system development. It covers pipeline creation, deployment, and integration with Swarm, showcasing how to use Vectorize's features and pre-built Swarm agent code for efficient data retrieval.

    2mFeb 8, 2025
    Free
    RAG Chatbot Tutorial using Vectorize, Elasticsearch, and Vercel
    Lesson 3

    RAG Chatbot Tutorial using Vectorize, Elasticsearch, and Vercel

    This lesson demonstrates building and deploying a Retrieval-Augmented Generation (RAG) chatbot using Vectorize, integrating LLMs like OpenAI and vector databases for efficient information retrieval. The process covers pipeline creation, data ingestion, chatbot integration, and deployment on platforms like Vercel, emphasizing ease of use and scalability.

    20mFeb 8, 2025
    Free
    Stop Making These 5 Mistakes With Your RAG Applications!
    Lesson 4

    Stop Making These 5 Mistakes With Your RAG Applications!

    Master Retrieval Augmented Generation (RAG) by leveraging vector databases for efficient similarity searches within unstructured data. Optimize your RAG system through strategic chunking, meticulous metadata design, and continuous data updates to ensure accuracy and real-time relevance.

    9mFeb 8, 2025
    Free
    Build a Slack AI assistant with n8n and Vectorize (that actually works!)
    Lesson 5

    Build a Slack AI assistant with n8n and Vectorize (that actually works!)

    This lesson shows how to build a Slack bot that uses Vectorize's RAG pipeline and OpenAI to answer questions. The process involves setting up a Slack app, configuring an n8n workflow to connect to Vectorize and OpenAI, and optimizing prompt engineering for accurate responses.

    25mMar 3, 2025
    Free
    How To Make RAG Models Perform Like A Pro
    Lesson 6

    How To Make RAG Models Perform Like A Pro

    Master efficient document searching with vectorize.io's RAG pipeline builder; optimize your search by testing various embedding models and chunking strategies to achieve optimal performance using metrics like NDCG and Recall.

    4mMar 3, 2025
    Free
    These RAG pipelines will change the way you build AI applications
    Lesson 7

    These RAG pipelines will change the way you build AI applications

    This lesson demonstrates building a Retrieval Augmented Generation (RAG) pipeline using the Vectorize platform to quickly process and query large PDF datasets, such as Denver's zoning regulations. The tutorial covers key steps including data ingestion from Dropbox, configuring extraction strategies, selecting a vector database (like Pinecone), and deploying a functional chatbot for efficient question answering.

    19mMar 3, 2025
    Free