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Foundations of Generative AI and LangChain

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Packt

Foundations of Generative AI and LangChain

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

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand key generative AI concepts and how they apply to LangChain.

  • Build and manage AI workflows using LangChain and LangGraph tools.

  • Implement prompt engineering and memory mechanisms for controlled output generation.

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Recently updated!

June 2026

Assessments

3 assignments

Taught in English

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This course is part of the Generative AI with LangChain Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

This module explores the evolution of generative AI, focusing on the distinctions between language models and AI agents, and the practical considerations for deploying them. Learners will compare open-source and closed-source LLMs, examine licensing implications, and gain hands-on familiarity with the LangChain framework for building modular AI applications.

What's included

1 video6 readings1 assignment

This module introduces you to the foundational concepts and practical setup required to build applications with LangChain. You'll learn how to configure API keys, explore different model providers, and understand the essential building blocks such as prompts, chains, and memory. The module also covers prompt engineering, running local versus cloud models, and the basics of multimodal AI, including image understanding.

What's included

1 video7 readings1 assignment

This module introduces LangGraph as a framework for building advanced workflows with large language models, focusing on state management, controlled output generation, and prompt engineering techniques. Learners will explore concepts such as reducers, memory mechanisms, and checkpointing to create robust, scalable applications. Practical strategies for zero-shot and few-shot prompting, as well as chain-of-thought reasoning, are also covered.

What's included

1 video9 readings1 assignment

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