From 134f24b4fb9b16c28fc864d3d2e2ec06381020d8 Mon Sep 17 00:00:00 2001 From: Maddie Duhon Date: Fri, 20 Sep 2024 10:19:38 -0400 Subject: [PATCH] Update case-study-dust.md added RAG guide --- qdrant-landing/content/blog/case-study-dust.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/case-study-dust.md b/qdrant-landing/content/blog/case-study-dust.md index 87f7b3614..62be7af77 100644 --- a/qdrant-landing/content/blog/case-study-dust.md +++ b/qdrant-landing/content/blog/case-study-dust.md @@ -52,7 +52,8 @@ data usually sits in various SaaS applications across the organization. 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. Users can manage so-called data sources within +aware AI assistants through [RAG](https://qdrant.tech/rag/rag-evaluation-guide/) +. Users can manage so-called data sources within Dust and upload files or directly connect to it via APIs to ingest data from tools like Notion, Google Drive, or Slack. Dust then handles the chunking strategy with the embeddings models and performs retrieval augmented generation.