Build, Analyze, and Refactor LLM Workflows
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Build, Analyze, and Refactor LLM Workflows
This course is part of Build Next-Gen LLM Apps with LangChain & LangGraph Specialization
Instructors: Starweaver
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What you'll learn
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.
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There are 3 modules in this course
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, youβll not only understand how to use LangChain effectively but also how to think like an AI systems engineerβbuilding intelligent, cost-efficient workflows that scale across diverse business contexts.
We'll transform raw API calls into modular LangChain components, exploring prompts, models, and parsers through hands-on examples.
What's included
4 videos2 readings1 peer review
4 videosβ’Total 30 minutes
- Welcome: Your LangChain Journeyβ’2 minutes
- Core Components Overviewβ’7 minutes
- Building Your First Chainβ’7 minutes
- Prompt Design and Parsingβ’14 minutes
2 readingsβ’Total 10 minutes
- Welcome to the Course: Course Overviewβ’5 minutes
- LangChain Component Architectureβ’5 minutes
1 peer reviewβ’Total 20 minutes
- Hands-On-Learning: Modularize Intent Classification: Refactor 2000-Line Chatbot with LangChainβ’20 minutes
We'll apply the proven 5-step methodology to systematically refactor existing LLM code into maintainable architectures.
What's included
3 videos1 reading1 peer review
3 videosβ’Total 29 minutes
- The 5-Step Blueprintβ’7 minutes
- Refactoring Demo Part 1β’11 minutes
- Refactoring Demo Part 2β’11 minutes
1 readingβ’Total 5 minutes
- Refactoring Best Practicesβ’5 minutes
1 peer reviewβ’Total 20 minutes
- Hands-On-Learning: Enterprise Document System Refactoring: From Legacy Code to LangChain Architectureβ’20 minutes
We'll implement battle-tested production patterns including RAG systems, caching strategies, and monitoring for scalable applications.
What's included
4 videos1 reading1 assignment2 peer reviews
4 videosβ’Total 33 minutes
- Introduction to RAGβ’8 minutes
- Building a RAG Systemβ’9 minutes
- Monitoring and Cachingβ’12 minutes
- Course Wrap-upβ’4 minutes
1 readingβ’Total 5 minutes
- Production Deployment Guideβ’5 minutes
1 assignmentβ’Total 20 minutes
- Build & Refactor LLM Workflows with LangChainβ’20 minutes
2 peer reviewsβ’Total 80 minutes
- Hands-On-Learning: Enterprise RAG Implementation: Production Q&A System with Sub-Second Responseβ’20 minutes
- Project: Build Complete Customer Support Assistant with LangChainβ’60 minutes
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