NextGen AI
Intermediate31% off

LangChain & LlamaIndex in Production

Build powerful LLM applications by integrating LangChain and LlamaIndex into production systems.

4.6
(3,100)
3,100 studentsEnglish

Created by Tom Becker · Staff Engineer

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This course includes

  • 15 video lessons
  • 4h 6m of content
  • Downloadable resources
  • Full lifetime access
  • Certificate of completion
  • 30-day money-back guarantee

What you'll learn

  • Build sophisticated RAG pipelines with LangChain and LlamaIndex.
  • Design and implement custom LLM agents capable of tool use.
  • Manage and query external data sources effectively.
  • Optimize LLM application performance for production.
  • Handle errors and edge cases in complex LLM workflows.
  • Integrate LLM applications with existing systems and APIs.
  • Understand the architectural differences between LangChain and LlamaIndex.
  • Implement best practices for security and maintainability.

About this course

Move beyond simple demos. This course focuses on the practical challenges and solutions for deploying LLM-powered applications using the leading orchestration frameworks: LangChain and LlamaIndex. We'll explore how to effectively connect LLMs with external data sources, manage complex agentic workflows, and ensure reliability and scalability in a production environment. You'll learn to leverage these tools to build sophisticated applications, such as RAG systems that access your company's knowledge base, agents that interact with APIs, and complex reasoning chains. We'll cover best practices for structuring your code, handling errors gracefully, and optimizing performance for real-world latency and throughput requirements. This isn't just about chaining calls; it's about building robust, maintainable LLM applications. We'll tackle common production issues like prompt injection vulnerabilities, managing state in agents, and integrating with vector databases. By the end, you'll be equipped to develop and deploy sophisticated LLM solutions that deliver tangible business value.

Course content

5 modules · 15 lessons · 4h 6m

  • Core Abstractions: Models, Prompts, and ChainsPreview
    15m
  • Building Simple Chains with `Runnable`Preview
    18m
  • Integrating Custom Logic into Chains
    12m

Requirements

  • Proficient Python programming skills.
  • Basic understanding of Large Language Models (LLMs).
  • Familiarity with API concepts.

Your instructor

T

Tom Becker

Staff Engineer

Tom has shipped production software for 12 years and is opinionated about what actually works with AI tools.