Build, Analyze, and Refactor LLM Workflows

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

• Construct modular LLM chains using LangChain's core components (prompts, models, and output parsers) to replace hardcoded API calls
• Apply systematic refactoring methodology to transform existing LLM scripts into maintainable LangChain workflows with proper error handling
• Implement production-ready patterns for common LLM use cases including Q&A systems, summarization pipelines, and data extraction workflows

• Basic familiarity with the topic and terminology
• Readiness for applied exercises