Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines

2026-03-19 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

This post details five caching mechanisms beyond basic Prompt Caching that can significantly reduce costs and latency in high-traffic AI applications, particularly those utilizing Retrieval Augmented Generation (RAG) pipelines. It distinguishes between exact-match caching, implemented with KV stores like Redis, and semantic caching, which uses vector databases such as ChromaDB for similarity-based retrieval. The article outlines how caching can be applied at various stages of a RAG pipeline, including query embedding, retrieval of document chunks, reranking results, prompt assembly, and caching entire query-response pairs. Each method aims to avoid redundant computations, such as regenerating embeddings or re-executing retrieval steps, by storing and reusing previously computed outputs.

Key takeaway

For AI Engineers building or optimizing RAG applications, integrating multiple caching layers beyond LLM-native prompt caching is crucial. You should implement query embedding, retrieval, reranking, prompt assembly, and query-response caches to minimize redundant computations. This approach will significantly reduce operational costs and improve response latency, especially in high-traffic enterprise deployments.

Key insights

Implementing diverse caching strategies across RAG pipelines significantly boosts efficiency and reduces operational costs.

Principles

• Cache frequently accessed components.
• Distinguish exact-match from semantic caching.
• Use external stores for RAG pipeline caches.

Method

Implement caching at query embedding, retrieval, reranking, prompt assembly, and query-response stages, using KV stores for exact matches and vector databases for semantic similarity.

In practice

• Use Redis for exact-match caching.
• Employ ChromaDB for semantic caching.
• Set distinct TTLs for different cache layers.

Topics

• LLM Caching
• RAG Pipeline Optimization
• Semantic Caching
• Exact-Match Caching
• Vector Databases

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

Related on AIssential

• Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines — Caching beyond prompts in RAG pipelines significantly reduces costs and latency by reusing query embeddings, retrieved documents, and full responses.
• What is Prompt Caching? Optimize LLM Latency with AI Transformers — Prompt caching stores precomputed LLM input representations (KV pairs) to reduce latency and cost for repeated prompt prefixes.
• Retrieval-Augmented Generation — A Deep Dive into Components — RAG improves LLM accuracy and relevance by integrating external knowledge retrieval with text generation.
• CacheWeaver: Cache-Aware Evidence Ordering for Efficient Grounded RAG Inference — CacheWeaver reorders RAG evidence to maximize prefix cache reuse, significantly reducing LLM prefill latency.
• Why Care About Prompt Caching in LLMs? — Prompt caching reduces LLM cost and latency by reusing results from repeated input tokens.
• How an Unexpected Reddit Spike Forced Me to Learn Prompt Caching the Hard Way — Multi-agent LLM pipelines offer significant prompt caching benefits, especially when context is passed sequentially between agents.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.