LangChain Expression Language (LCEL) Explained: Chains, Runnables & Fallbacks (Ep. 5)

Welcome to Episode 5 of our Generative AI Series! 🚀 In this video, we take a deep dive into LangChain Expression Language (LCEL), the latest innovation for building advanced AI workflows. Whether you're new to LCEL or looking to master its features, this video is packed with practical insights.

Here’s what you’ll learn:

What is LCEL, and how does it simplify chain creation?

The difference between simple chains and LCEL chains.

How to use runnables like RunnablePassthrough, RunnableLambda, and RunnableParallel.

Implementing fallbacks for robust workflows.

A hands-on example of an LCEL chain in a typical RAG (Retrieval-Augmented Generation) application.

🔹 Key Takeaways:

Build modular and flexible workflows using LCEL’s pipe operator (|).

Use parallel execution, custom logic, and fallback mechanisms to handle complex tasks efficiently.

Learn how LCEL can optimize runtime performance for scalable AI applications.

💡 Whether you're an AI developer or enthusiast, this video will equip you with the tools to create efficient and robust LangChain workflows.

📌 Missed earlier episodes? Watch them now to learn about RAG components, LangChain basics, and more!

If you find this video helpful, don’t forget to like, subscribe, and hit the notification bell for weekly updates. Let’s build the future of AI together!

#LangChain #LCEL #GenerativeAI #Runnables #AITutorial #MachineLearning