LLM Engineering: Build Production-Ready AI Systems

A warm welcome to LLM Engineering: Build Production-Ready AI Systems course by Uplatz. Large Language Models (LLMs) are the AI systems behind tools like ChatGPT—models trained on massive amounts of text so they can understand instructions, generate content, reason over context, and call tools to complete tasks. But building real, reliable, production-grade LLM applications requires much more than “just prompting.

”That’s where the modern LLM engineering stack comes in:Prompting & Prompt Engineering: Designing instructions (system + user prompts) so the model behaves consistently, safely, and predictably. RAG (Retrieval-Augmented Generation): A technique that lets an LLM use your own documents/data (PDFs, knowledge bases, product docs, policies) by retrieving relevant context at runtime—dramatically reducing hallucinations and keeping answers grounded. LangChain: A powerful framework to build LLM applications using modular building blocks—prompts, chains, tools, agents, memory, retrievers, output parsers, and integrations.

LangGraph: A framework for building stateful, multi-step, agentic workflows as graphs—ideal for multi-agent systems, conditional routing, retries, loops, long-running flows, and robust orchestration. LangSmith: An observability + evaluation platform that helps you trace LLM calls, debug prompt/chain failures, measure quality, run evaluations, and monitor performance as you iterate toward production. In this course, you will learn the complete end-to-end skillset of LLM Engineering—from foundations and prompting to RAG, agents, observability, security, testing, optimization, and production deployment.

What you’ll build in this courseThis is a hands-on, engineering-focused course where you’ll progressively build the core pieces of modern LLM systems, including:Prompting systems that are structured, reliable, and scalableRAG pipelines that connect LLMs to real documents and private knowledgeAgentic workflows using LangGraph with routing, retries, and stateObservable and testable LLM applications with LangSmith traces + evaluationsMultimodal applications (text + vision/audio/tool use patterns)Production patterns for performance, cost control, and reliabilityA complete capstone LLM system built end-to-endWhy this course is differentMost LLM content online stops at basic prompting or a few small demos. This course is designed to take you from “I can call an LLM” to “I can engineer a production-grade LLM system.