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.

In this lesson

01Learning Objectives
02The Problem
03The Concept
04Build it
05Use it
06Exercises
07Key Terms
08Further Reading

About 5900 words of reading, with code, 5 quiz questions and the tutor.

The full lesson opens with an access code

You can see what this lesson covers and the milestone it sets. The full text, code, quizzes, the tutor and the AI reviews come with the K850 practice, along with weekly build sessions and code review.

Book the K850 practice Read an open lesson

Milestone forA production labour-compliance assistant

Ship it as a service

Package the assistant as an API with authentication by role, rate limits, streaming, timeouts, retries and a health check, with a small chat interface that shows citations and handles dropped mobile connections.

Done when

Role-authenticated API with per-user rate limits
Streaming with a 20-second timeout and one retry
A chat UI that shows citations and recovers from a dropped connection mid-answer
p95 latency under 6 seconds on the golden set

Stretch: Load test at 20 concurrent users and report errors

Paste your evidence: the README section, results tables, numbers, test output

Reviews open with an access code.

Interview practice

Questions on this topic that AI engineering interviews ask, with the companies reported to ask them. Answer the way you would out loud; Jev scores it and DeepSeek tells you what to add.

Your answer

Scoring opens with an access code. You can still read the questions and prepare.

Adapted from open-source work: AI Engineering from Scratch by Rohit Ghumare (MIT, lesson text, code and quizzes); Awesome LLM Apps by Shubham Saboo (Apache-2.0, starter code); AI Engineering Interview Questions by Outcome School (Apache-2.0, interview questions). Capstones, milestones, data packs and Zambian context by Zambrite.