LLM Engineering: Build Production-Ready AI Systems

About this course

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 deploym

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