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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Integrating Caching into a RAG Pipeline

Implement caching layers within your RAG application to store and retrieve previously generated responses or retrieved contexts.

Intro to RAG Caching

Welcome to the final lesson on caching! We've learned why caching is vital and explored different strategies.

Now, let's get practical. This lesson focuses on integrating caching directly into your RAG pipeline to boost performance and cut costs.

Where to Cache in RAG

In a RAG pipeline, there are two primary points where caching offers significant benefits:

  • Retrieval Step: Caching the documents retrieved by your vector database.
  • Generation Step: Caching the final response generated by the Large Language Model (LLM).

Each point addresses different bottlenecks.

All lessons in this course

  1. The Importance of Caching LLM Calls
  2. In-Memory and External Caching Strategies
  3. Integrating Caching into a RAG Pipeline
  4. Semantic Caching for LLM Apps
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