---
title: Learn how to get started with Qdrant for your RAG use case
features:
- id: 0
  image:
    src: /img/retrieval-augmented-generation-use-cases/case1.svg
    srcMobile: /img/retrieval-augmented-generation-use-cases/case1-mobile.svg
    alt: Music recommendation
  title: Question and Answer System with LlamaIndex
  description: Combine Qdrant and LlamaIndex to create a self-updating Q&A system.
  link:
    text: Jupyter Notebook
    url: https://github.com/qdrant/examples/blob/949669f001a03131afebf2ecd1e0ce63cab01c81/llama_index_recency/Qdrant%20and%20LlamaIndex%20%E2%80%94%20A%20new%20way%20to%20keep%20your%20Q%26A%20systems%20up-to-date.ipynb
- id: 1
  image:
    src: /img/retrieval-augmented-generation-use-cases/case2.svg
    srcMobile: /img/retrieval-augmented-generation-use-cases/case2-mobile.svg
    alt: Food discovery
  title: Retrieval Augmented Generation with OpenAI and Qdrant
  description: Basic RAG pipeline with Qdrant and OpenAI SDKs.
  link:
    text: Learn More
    url: /articles/food-discovery-demo/
caseStudy:
  logo:
    src: /img/retrieval-augmented-generation-use-cases/customer-logo.svg
    alt: Logo
  title: See how Dust is using Qdrant for RAG
  description: Dust provides companies with the core platform to execute on their GenAI bet for their teams by deploying LLMs across the organization and providing context aware AI assistants through RAG.
  link:
    text: Read Case Study
    url: /blog/dust-and-qdrant/
  image:
    src: /img/retrieval-augmented-generation-use-cases/case-study.png
    alt: Preview
sitemapExclude: true
---