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Update semantic-cache-ai-data-retrieval.md

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added rag guide link
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Maddie Duhon
2024-09-20 10:16:56 -04:00
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@@ -39,9 +39,9 @@ In this blog and video, we will walk you through how to use Qdrant to implement
Semantic cache is increasingly used in Retrieval-Augmented Generation (RAG) applications. In RAG, when a user asks a question, we embed it and search our vector database, either by using keyword, semantic, or hybrid search methods. The matched context is then passed to a Language Model (LLM) along with the prompt and user question for response generation.
Qdrant is recommended for setting up semantic cache as semantically evaluates the response. When semantic cache is implemented, we store common questions and their corresponding answers in a key-value cache. This way, when a user asks a question, we can retrieve the response from the cache if it already exists.
Qdrant is recommended for setting up semantic cache as semantically [evaluates](https://qdrant.tech/rag/rag-evaluation-guide/) the response. When semantic cache is implemented, we store common questions and their corresponding answers in a key-value cache. This way, when a user asks a question, we can retrieve the response from the cache if it already exists.
**Diagram:** Semantic cache improves RAG by directly retrieving stored answers to the user. **Follow along with the gif** and see how semantic cache stores and retrieves answers.
**Diagram:** Semantic cache improves [RAG](https://qdrant.tech/rag/rag-evaluation-guide/) by directly retrieving stored answers to the user. **Follow along with the gif** and see how semantic cache stores and retrieves answers.
![Alt Text](/blog/semantic-cache-ai-data-retrieval/semantic-cache.gif)