Llm Engineering: Build Production-Ready Ai Systems
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Llm Engineering: Build Production-Ready Ai Systems
Published 2/2026
Created by Uplatz Training
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 39 Lectures ( 17h 34m ) | Size: 11.1 GB [/center] Build production-ready LLM apps using LangChain, RAG, agents, multimodal AI, deployment, and real-world systems What you'll learn
✓ Understand how large language models work, including tokens, context windows, and inference
✓ Design effective prompts and prompt strategies for reliable and controllable LLM behavior
✓ Build modular LLM pipelines using LangChain core components
✓ Implement Retrieval-Augmented Generation (RAG) systems with embeddings and vector databases
✓ Design agentic and stateful workflows using LangGraph
✓ Debug, trace, and evaluate LLM applications using LangSmith
✓ Build multimodal LLM applications combining text, images, audio, and tools
✓ Engineer production-ready LLM systems with scalability, reliability, and cost control
✓ Apply security, safety, and governance best practices to LLM applications
✓ Test, benchmark, and optimize LLM pipelines for quality, latency, and cost
✓ Design and deliver a complete end-to-end LLM system as a capstone project Requirements
● Enthusiasm and determination to make your mark on the world! 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 Who this course is for
■ Software engineers and backend developers who want to build real-world applications powered by large language models
■ Machine learning and AI engineers looking to move beyond model training into LLM application engineering
■ Data scientists who want to deploy LLM-powered systems such as RAG pipelines and agents
■ Python developers interested in working with LangChain, LangGraph, and modern LLM frameworks
■ Startup founders and product builders building AI-driven products and agentic systems
■ MLOps / Platform engineers involved in deploying, monitoring, and scaling LLM applications
■ Professionals transitioning into AI engineering roles with hands-on, production-grade skills
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Llm Engineering: Build Production-Ready Ai Systems
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