Build and Ship LLM Applications That Work at Scale
Large language models are only useful when they run reliably in production. This track covers the full stack of techniques that make LLM-powered apps fast, accurate, and cost-effective: Retrieval-Augmented Generation, vector databases, response caching, and production deployment patterns. The focus is entirely practical — every concept maps directly to code you would write in a real system.
What You Will Learn
You will start with RAG fundamentals and learn to wire an LLM to a retrieval pipeline that fetches relevant context at query time. From there you will configure and query vector databases, implement caching layers that cut latency and API costs, and build robust data ingestion and preprocessing pipelines. Later courses cover evaluation and testing methodologies for RAG systems, Advanced RAG Techniques for precision and recall, and Security and Reliability in LLM Production — including rate limiting, failover, and prompt injection defenses.
The Learning Path
Twelve courses span B1 through C2. The first course, Introduction to LLM Production & RAG Fundamentals, is free and establishes the mental model. B1–B2 courses build the working pipeline: your first RAG app, vector DB integration, caching, data preprocessing, and evaluation. C1 courses raise the bar with Advanced RAG Techniques for Enhanced Performance, Optimizing RAG Performance and Cost Efficiency, and Production Deployment Strategies for LLM Apps. The track finishes at C2 with Scaling and Monitoring LLM Applications, covering distributed load, observability, and alerting for live systems.
How It Works
Each course is split into short, hands-on lessons you complete in the built-in editor with real-time feedback and an AI tutor available when you get stuck. Exercises use realistic data and service configurations so what you practice reflects what production systems actually require.
