Make LLM Agents Faster and Cheaper with Semantic Caching & Reranking (Production-Ready Agents #1)

Description

Your LLM agents are slow and burning cash because they repeat the same expensive calls over and over. In this video, I show how to fix that by adding semantic caching and reranking. A missing system layer that can dramatically reduce latency and cost (50-70%) in LLM-based applications. I’ll walk through: • Exact vs fuzzy vs semantic caching • Different reranking strategies (cross-encoders vs LLM rerankers) • How to tune caching on your own data using a Streamlit dashboard • A full RAG agent equipped with semantic caching Although I demonstrate everything using a RAG setup, these techniques apply to any LLM system or agent: planners, tool-using agents, conversational AI, chatbots, and more. This is a practical, production-oriented walkthrough, not just theory. 🔗 RESOURCES: 📂 Semantic Caching & Reranking Repo (full code & demos): https://github.com/Farzad-R/Agent-Factory 📂 Embedding Repo: https://github.com/Farzad-R/LLM-Zero-to-Hundred/tree/master/tutorials/text_embedding_tutorial ⏱️TIMESTAMPS: (00:00:00) Intro (00:00:39) Semantic Caching & Reranking Concepts (00:15:00) Project Overview & Architecture (00:18:37) Basic Implementation Guide (00:26:20) Advanced Implementation with Reranking (00:58:44) Dashboard Tutorial (Test Your Own Data) (01:12:18) RAG Agent Demo with Semantic Caching 💡 WHAT YOU'LL GET: ✓ Complete semantic caching implementation ✓ Interactive dashboard for testing strategies ✓ Production-ready RAG chatbot example ✓ Jupyter notebooks with step-by-step tutorials ✓ All code open-source and ready to use 🌐 CONNECT WITH ME: • LinkedIn: farzad-roozitalab • X: Farzad_Rzt 📌 KEY CONCEPTS COVERED: #SemanticCaching #LLMOptimization #RAG #VectorEmbeddings #LLMAgents #AIEngineering #CostOptimization #LatencyReduction #ProductionAI #MachineLearning 🔔 Don't forget to LIKE and SUBSCRIBE if this helps you build better AI systems!