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 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.”You will learn:How to design LLM applications like real software systemsHow to measure quality (not just “it seems good”)How to add guardrails, safety, and governanceHow to optimize for latency and costHow to make applications maintainable as they growWhat you’ll learnBy 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 productsBuild 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 patternsApply security, safety, and governance practices (prompt injection, data leakage, guardrails)Test, benchmark, and optimize LLM pipelines for quality, latency, and costDeliver an end-to-end capstone project you can showcase in your portfolioWho this course is forPython developers who want to build real LLM-powered applicationsSoftware engineers building AI features into productsAI/ML engineers moving into LLM application engineeringData scientists who want to ship LLM apps (not just experiments)Startup founders and product builders building agentic toolsMLOps/platform engineers working on LLM deployment and monitoringLLM Engineering: Build Production-Ready AI Systems – Course CurriculumModule 1: Foundations & Environment SetupIntroduction to LLM EngineeringLLM Ecosystem OverviewPython, Packages, and Tooling SetupDevelopment Environment ConfigurationModule 2: LLM Fundamentals & Prompt Engineering MasteryHow Large Language Models WorkTokens, Context Windows, and InferencePrompt Engineering TechniquesSystem, User, and Tool PromptsPrompt Optimization and Best PracticesModule 3: LangChain Core EssentialsPart 1: LangChain FundamentalsLangChain Architecture and ConceptsLLM Wrappers and Prompt TemplatesChains and Execution FlowPart 2: Advanced Chains and ComponentsSequential and Router ChainsMemory Types and Usage PatternsOutput Parsers and Structured ResponsesPart 3: Real-World LangChain PatternsTool Calling and Agent BasicsError Handling and GuardrailsBuilding Modular LangChain PipelinesModule 4: Retrieval-Augmented Generation (RAG) MasteryPart 1: RAG FoundationsWhy RAG MattersEmbeddings and Vector StoresChunking and Indexing StrategiesPart 2: Advanced RAG SystemsHybrid Search and Re-rankingMetadata Filtering and Context ControlBuilding End-to-End RAG PipelinesModule 5: LangGraph – Agentic & Stateful WorkflowsPart 1: LangGraph FundamentalsWhy LangGraphGraph-Based Agent DesignNodes, Edges, and StatePart 2: Multi-Agent WorkflowsConditional Flows and BranchingStateful ConversationsTool-Oriented Graph DesignPart 3: Advanced Agent OrchestrationError Recovery and LoopsLong-Running AgentsScalable Agent ArchitecturesModule 6: LangSmith – Observability, Debugging & EvaluationPart 1: LangSmith IntroductionTracing LLM CallsUnderstanding Execution GraphsPart 2: Debugging & MonitoringPrompt and Chain DebuggingLatency and Cost AnalysisPart 3: Evaluation & Feedback LoopsDataset-Based EvaluationsHuman-in-the-Loop FeedbackPart 4: Performance & Quality MetricsAccuracy, Relevance, and Hallucination TrackingRegression DetectionPart 5: Production ReadinessContinuous Evaluation PipelinesBest Practices for Enterprise UsageModule 7: Multimodal & Advanced LLM TechniquesPart 1: Multimodal LLM FoundationsText, Image, Audio, and Video ModelsMultimodal Prompting BasicsPart 2: Vision + Language SystemsImage Understanding and ReasoningOCR and Visual QAPart 3: Audio & Speech IntegrationSpeech-to-Text and Text-to-SpeechConversational Audio SystemsPart 4: Tool-Using Multimodal AgentsVision + ToolsMultimodal Function CallingPart 5: Advanced Prompt & Context StrategiesCross-Modal Context ManagementMemory for Multimodal SystemsPart 6: Multimodal RAGImage and Document RetrievalPDF and Knowledge Base PipelinesPart 7: Optimization TechniquesLatency ReductionCost OptimizationPart 8: Real-World Multimodal ArchitecturesEnterprise Use CasesDesign PatternsModule 8: Production LLM EngineeringPart 1: Production ArchitectureLLM System DesignAPI-Based and Service-Oriented ArchitecturesPart 2: Deployment StrategiesModel Hosting OptionsCloud and Self-Hosted LLMsPart 3: Scaling & ReliabilityLoad HandlingRate Limiting and FallbacksPart 4: Cost ManagementToken OptimizationCaching StrategiesPart 5: Logging & MonitoringMetrics and AlertsIncident HandlingPart 6: CI/CD for LLM SystemsPrompt VersioningAutomated Testing PipelinesModule 9: LLM Security, Safety & GovernancePrompt Injection AttacksData Leakage RisksHallucinations, Bias & AlignmentAuditability, Compliance & GovernanceEnterprise Guardrails & Access ControlModule 10: Testing, Benchmarking & OptimizationLLM Testing StrategiesBenchmarking Models & PipelinesPrompt and System OptimizationContinuous Improvement LoopsModule 11: Capstone Project – End-to-End LLM SystemCapstone Planning & ArchitectureFull System ImplementationDeployment, Evaluation & Final Review