---
title: "Discussion on \"Semantic Caching for LLM Applications: A Practical Guide for Agentic AI Systems\" | Hashnode"
description: "Discussion on \"Semantic Caching for LLM Applications: A Practical Guide for Agentic AI Systems\". Learn how semantic caching reduces LLM latency and cost in production Agentic AI systems using practical strategies and architecture patterns."
url: "https://hashnode.com/posts/semantic-caching-for-llm-applications-a-practical-guide-for-agentic-ai-systems/6aba49d49039069af6dd0bbc"
---

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# [Semantic Caching for LLM Applications: A Practical Guide for Agentic AI Systems](https://ai-in-production.hashnode.dev/semantic-caching-for-llm-applications-a-practical-guide-for-agentic-ai-systems?utm_source=hashnode&utm_medium=feed)

Learn how semantic caching reduces LLM latency and cost in production Agentic AI systems using practical strategies and architecture patterns.

Sep 28 · 15 min read · ai-in-production.hashnode.dev  
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## Written by

[Bonny Philip](https://hashnode.com/@philipbonny)

@philipbonny · Bangalore, India

  
[View profile](https://hashnode.com/@philipbonny)

Engineering production-grade RAG, Agentic AI, and LLM systems -sharing my experience

Lead AI/ML Engineer with 7.5 years of experience designing and productionizing Agentic/Gen AI systems and machine learning solutions from the ground up. GenAI system architect - capable of taking an AI requirement from initial problem framing, architecture design, multi-agent orchestration, and model development, all the way to a hardened, monitored production system. Skilled at architecting distributed, scalable AI systems that integrate enterprise data with intelligent agent workflows, and at making the cost/latency/reliability tradeoffs needed to run GenAI systems in production. Strong background in Responsible AI and AI governance, with experience designing evaluation and monitoring modules for bias, variance, groundedness, toxicity, and model risk mitigation to ensure ethical and compliant AI systems. Strong track record of owning the entire lifecycle — from initial design to deployment and operational stability — while delivering measurable business impact.

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