AI Engineering/Phase 11/Lesson 13

Building a Production LLM Application

You have built prompts, embeddings, RAG pipelines, function calling, caching layers, and guardrails. Separately. In isolation. Like practicing guitar scales without ever playing a song. This lesson is the song. You will wire every component from Lessons 01-12 into a single production-ready service. Not a toy. Not a demo. A system that handles real traffic, fails gracefully, streams tokens, tracks costs, and survives its first 10,000 users.

Build (Capstone) 2 hr Python

Prerequisites: Phase 11 Lessons 01-15

#Learning Objectives

Wire all Phase 11 components (prompts, RAG, function calling, caching, guardrails) into a single production-ready service
Implement streaming token delivery, graceful error handling, and request timeout management
Build observability into the application: request logging, cost tracking, latency percentiles, and error rate dashboards
Deploy the application with health checks, rate limiting, and a fallback strategy for provider outages

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.