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.

Build, Analyze, and Refactor LLM Workflows

Build, Analyze, and Refactor LLM Workflows
This course is part of Build Next-Gen LLM Apps with LangChain & LangGraph Specialization


Instructors: Starweaver
Access provided by Paidy
Recommended experience
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.
Skills you'll gain
Details to know

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December 2025
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There are 3 modules in this course
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
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
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
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