Day 13: Build an AI Caching System | 21 Days of FDE Projects

In Day 13 of the 21 Days of FDE Projects series, we build a simple AI caching system using Python, Google Colab and Groq. The idea is simple: if the same or similar request has already been answered, we should avoid calling the LLM again. Instead, we first check the cache. Workflow: User Request → Check Cache → Cache Hit / Cache Miss → Call LLM if Needed → Save Response → Return Answer This project introduces an important FDE concept: caching for speed, cost reduction and scalability. We also look at the difference between: Cache Hit = the answer is already stored Cache Miss = the system needs to fetch or generate it In production, caching can help reduce repeated LLM calls, lower latency and make AI systems more efficient. Built with Google Colab + Python + Groq API. Hashtags: #FDE #ForwardDeployedEngineer #AIEngineering #Caching #LLM #Groq #Python #AIProjects #GoogleColab #aiengineering YouTube Tags: FDE, Forward Deployed Engineer, AI Caching, Cache Hit, Cache Miss, Semantic Caching, LLM Caching, Groq API, Python AI Project, AI Engineer, Google Colab, AI Tutorial, AI Optimization, LLM Cost Optimization, AI Performance, FDE Projects, 21 Days of FDE Projects