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URL: https://www.coursera.org/learn/build-analyze-refactor-llm-workflows

⇱ Build, Analyze, and Refactor LLM Workflows | Coursera


Build, Analyze, and Refactor LLM Workflows

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Build, Analyze, and Refactor LLM Workflows

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

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.

Details to know

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Assessments

1 assignmentΒΉ

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Build Next-Gen LLM Apps with LangChain & LangGraph Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

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, you’ll not only understand how to use LangChain effectively but also how to think like an AI systems engineerβ€”building 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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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

ΒΉ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.