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Build, Analyze, and Refactor LLM Workflows · LearnSpace
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Build, Analyze, and Refactor LLM Workflows

Курс от Coursera
Средний≈ 7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Master the art of building production-ready LLM applications with LangChain, the framework powering 82% of enterprise GPT deployments. This comprehensive intermediate course transforms you from writing brittle LLM scripts to architecting scalable AI solutions used by Fortune 500 companies. Starting with fragmented code full of hardcoded prompts and raw API calls, you'll learn to construct elegant modular chains that are maintainable, testable, and secure. Through three progressive modules, you'll discover how industry leaders reduce development time by 65% and cut operational costs by 60% using LangChain patterns. This course is designed for intermediate Python developers with experience using APIs and familiarity with large language models (LLMs). If you're looking to elevate your skills by mastering LangChain and building scalable, production-ready LLM applications, this course is for you. Learn how to refactor fragmented LLM scripts into elegant, maintainable workflows that can be used by enterprise-level applications, cutting development time and operational costs. Perfect for developers aiming to implement robust LLM solutions in real-world scenarios. To succeed in this course, learners should have a basic understanding of Python programming and experience with API usage for integrating external services. Familiarity with large language models (LLMs) and their common use cases, such as text generation or classification, will also be beneficial, as the course focuses on building applications that leverage LLMs. By the end of this course, you’ll not only understand how to use LangChain effectively but also how to think like an AI systems engineer—building intelligent, cost-efficient workflows that scale across diverse business contexts.

Навыки, которые вы освоите

LangChainCode ReusabilityLLM ApplicationRetrieval-Augmented GenerationEnterprise Application ManagementLarge Language ModelingApplication DeploymentAI WorkflowsOperational EfficiencyPrompt EngineeringEmbeddingsPrompt PatternsSystem MonitoringMaintainabilityWorkflow ManagementProcess OptimizationScalability

Программа курса

3 модулей · 21 учебных материалов

01Foundations: From API Calls to LangChain Components8 материалов

Lesson 1: Foundations: From API Calls to LangChain Components

Your First Day as a LLM DeveloperDIALOGUEWelcome to the Course: Course OverviewЧтениеWelcome: Your LangChain JourneyВидеоCore Components OverviewВидео

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Starweaver

Global Leaders in Professional & Technology Education

Ritesh Vajariya

Advisor | Leader | Speaker |Author

Build, Analyze, and Refactor LLM Workflows
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Обучение на Coursera

≈ 7 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Испанский, Японский

Часть программы вашего университета
Building Your First ChainВидео
Prompt Design and ParsingВидео
LangChain Component ArchitectureЧтение
Designing Modular LLM Architectures for Production SystemsЗадание
02Refactoring Methodology: Systematic Code Transformation5 материалов

Lesson 2: Refactoring Methodology: Systematic Code Transformation

Refactoring Strategy WorkshopDIALOGUEThe 5-Step BlueprintВидеоRefactoring Demo Part 1ВидеоRefactoring Demo Part 2ВидеоSystematic Refactoring Blueprint for Production-Grade SystemsЗадание
03Production Patterns: Building Robust LLM Applications8 материалов

Lesson 3: Production Patterns: Building Robust LLM Applications

Production Deployment ChallengeDIALOGUEIntroduction to RAGВидеоBuilding a RAG SystemВидеоProduction Deployment GuideЧтениеMonitoring and CachingВидеоCourse Wrap-upВидеоEnterprise RAG & LLM Architecture CapstoneЗаданиеBuild & Refactor LLM Workflows with LangChainЗадание