[Review] LangChain for LLM Application Development

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Quick take

Today, I completed a concise course on Langchain offered by deeplearning.ai. In this course, Andrew Ng collaborates with Harrison Chase, not the creator of Langchain, but an expert in the field.

This is my second course on LLMs. Despite its brevity, the course offers a comprehensive overview of Langchain. Even though the course duration is listed as one hour, I invested over five hours due to taking notes, running my own examples, and some minor debugging.

The course delves into topics such as:

Models, prompts & parsers.
Memory handling.
Chains.
Question & answer.
Evaluation (with an Easter egg).
Agents.

The course emphasizes hands-on learning, providing a collaborative notebook for each section. Every segment is practical, packed with relevant examples, ensuring learners grasp how Langchain operates in real-life scenarios.

I would wholeheartedly recommend this course to anyone keen on developing applications powered by LLMs.

Link:

LangChain for LLM Application Development - DeepLearning.AI

The framework to take LLMs out of the box. Learn to use LangChain to call LLMs into new environments, and use memories, chains, and agents to take on new and complex tasks.