LLM Engineering: Build Production-Ready AI Systems
Course Description
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 course
This 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 scalable
RAG pipelines that connect LLMs to real documents and private knowledge
Agentic workflows using LangGraph with routing, retries, and state
Observable and testable LLM applications with LangSmith traces + evaluations
Multimodal applications (text + vision/audio/tool use patterns)
Production patterns for performance, cost control, and reliability
A complete capstone LLM system built end-to-end
Why this course is different
Most 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.”
You will learn:
How to design LLM applications like real software systems
How to measure quality (not just “it seems good”)
How to add guardrails, safety, and governance
How to optimize for latency and cost
How to make applications maintainable as they grow
What you’ll learn
By the end of this course, you will be able to:
Understand how LLMs work (tokens, context windows, inference, limitations)
Master prompting patterns used in real LLM products
Build modular pipelines using LangChain (prompts, chains, tools, agents)
Implement production-grade RAG (chunking, embeddings, retrieval, reranking concepts)
Build stateful and agentic workflows with LangGraph (graphs, nodes, state, routing)
Trace, debug, evaluate, and monitor apps using LangSmith (quality + performance)
Apply multimodal patterns (text + image/audio/tool workflows)
Engineer production systems: scaling, cost optimization, caching, reliability patterns
Apply security, safety, and governance practices (prompt injection, data leakage, guardrails)
Test, benchmark, and optimize LLM pipelines for quality, latency, and cost
Deliver an end-to-end capstone project you can showcase in your portfolio
Who this course is for
Python developers who want to build real LLM-powered applications
Software engineers building AI features into products
AI/ML engineers moving into LLM application engineering
Data scientists who want to ship LLM apps (not just experiments)
Startup founders and product builders building agentic tools
MLOps/platform engineers working on LLM deployment and monitoring
LLM Engineering: Build Production-Ready AI Systems - Course Curriculum
Module 1: Foundations & Environment Setup
Introduction to LLM Engineering
LLM Ecosystem Overview
Python, Packages, and Tooling Setup
Development Environment Configuration
Module 2: LLM Fundamentals & Prompt Engineering Mastery
How Large Language Models Work
Tokens, Context Windows, and Inference
Prompt Engineering Techniques
System, User, and Tool Prompts
Prompt Optimization and Best Practices
Module 3: LangChain Core Essentials
Part 1: LangChain Fundamentals
LangChain Architecture and Concepts
LLM Wrappers and Prompt Templates
Chains and Execution Flow
Part 2: Advanced Chains and Components
Sequential and Router Chains
Memory Types and Usage Patterns
Output Parsers and Structured Responses
Part 3: Real-World LangChain Patterns
Tool Calling and Agent Basics
Error Handling and Guardrails
Building Modular LangChain Pipelines
Module 4: Retrieval-Augmented Generation (RAG) Mastery
Part 1: RAG Foundations
Why RAG Matters
Embeddings and Vector Stores
Chunking and Indexing Strategies
Part 2: Advanced RAG Systems
Hybrid Search and Re-ranking
Metadata Filtering and Context Control
Building End-to-End RAG Pipelines
Module 5: LangGraph – Agentic & Stateful Workflows
Part 1: LangGraph Fundamentals
Why LangGraph
Graph-Based Agent Design
Nodes, Edges, and State
Part 2: Multi-Agent Workflows
Conditional Flows and Branching
Stateful Conversations
Tool-Oriented Graph Design
Part 3: Advanced Agent Orchestration
Error Recovery and Loops
Long-Running Agents
Scalable Agent Architectures
Module 6: LangSmith – Observability, Debugging & Evaluation
Part 1: LangSmith Introduction
Tracing LLM Calls
Understanding Execution Graphs
Part 2: Debugging & Monitoring
Prompt and Chain Debugging
Latency and Cost Analysis
Part 3: Evaluation & Feedback Loops
Dataset-Based Evaluations
Human-in-the-Loop Feedback
Part 4: Performance & Quality Metrics
Accuracy, Relevance, and Hallucination Tracking
Regression Detection
Part 5: Production Readiness
Continuous Evaluation Pipelines
Best Practices for Enterprise Usage
Module 7: Multimodal & Advanced LLM Techniques
Part 1: Multimodal LLM Foundations
Text, Image, Audio, and Video Models
Multimodal Prompting Basics
Part 2: Vision + Language Systems
Image Understanding and Reasoning
OCR and Visual QA
Part 3: Audio & Speech Integration
Speech-to-Text and Text-to-Speech
Conversational Audio Systems
Part 4: Tool-Using Multimodal Agents
Vision + Tools
Multimodal Function Calling
Part 5: Advanced Prompt & Context Strategies
Cross-Modal Context Management
Memory for Multimodal Systems
Part 6: Multimodal RAG
Image and Document Retrieval
PDF and Knowledge Base Pipelines
Part 7: Optimization Techniques
Latency Reduction
Cost Optimization
Part 8: Real-World Multimodal Architectures
Enterprise Use Cases
Design Patterns
Module 8: Production LLM Engineering
Part 1: Production Architecture
LLM System Design
API-Based and Service-Oriented Architectures
Part 2: Deployment Strategies
Model Hosting Options
Cloud and Self-Hosted LLMs
Part 3: Scaling & Reliability
Load Handling
Rate Limiting and Fallbacks
Part 4: Cost Management
Token Optimization
Caching Strategies
Part 5: Logging & Monitoring
Metrics and Alerts
Incident Handling
Part 6: CI/CD for LLM Systems
Prompt Versioning
Automated Testing Pipelines
Module 9: LLM Security, Safety & Governance
Prompt Injection Attacks
Data Leakage Risks
Hallucinations, Bias & Alignment
Auditability, Compliance & Governance
Enterprise Guardrails & Access Control
Module 10: Testing, Benchmarking & Optimization
LLM Testing Strategies
Benchmarking Models & Pipelines
Prompt and System Optimization
Continuous Improvement Loops
Module 11: Capstone Project – End-to-End LLM System
Capstone Planning & Architecture
Full System Implementation
Deployment, Evaluation & Final Review
Related Free Courses
Global Career Development, Job Search, Interviewing Skills
Django Masterclass: Get Started With Django Web Development
Understanding TypeScript For Beginner To Advanced
