Generative AI with LangChain

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This course is a comprehensive exploration of LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into. This resource explores the power of large language models (LLMs) and how to harness them effectively using LangChain. It covers practical applications, from building chatbots to automating data analysis, and provides insights into fine-tuning and deployment. Designed for developers and researchers, it offers a clear path to mastering generative AI tools. This resource is ideal for developers and researchers interested in leveraging LLMs with LangChain. A basic understanding of Python is required, and familiarity with machine learning concepts will enhance the learning experience. This course is a practical guide to understanding how to build LLM applications with LangChain and deepen your understanding of LLMs. Written in a clear and concise style, it balances theory appropriately, offering plenty of code examples to help readers learn with practice. This course is based on Generative AI with LangChain, by Ben Auffarth. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

What Is Generative AI?
- This module provides an in-depth introduction to generative AI and large language models (LLMs), covering their evolution, training processes, and real-world applications. Learners will explore how these models function across multiple domains and how to access and use them effectively. The module also clarifies common misconceptions and highlights the potential of AI beyond popular media portrayals.
LangChain for LLM Apps
- This module provides an in-depth look at LangChain, an open-source framework for building intelligent applications using large language models. Learners will explore how to integrate LLMs with external data and tools to create dynamic, data-aware systems, and understand key components like chains, agents, and memory.
Getting Started with LangChain
- This module provides an introduction to setting up and integrating AI models using LangChain, covering environment configuration, cloud model access, and practical implementation of LLMs and text-to-image models. Learners will gain hands-on experience with dependency management, API key security, and prompt engineering to build efficient AI applications.
Building Capable Assistants
- This module teaches how to enhance large language models (LLMs) by integrating tools, extracting structured data from documents, and mitigating hallucinations through fact-checking. Learners will gain practical skills in building more reliable and capable AI assistants using frameworks like LangChain.
Building a Chatbot like ChatGPT
- This module covers techniques for building intelligent chatbots by integrating retrieval-augmented generation (RAG), vector storage, and memory mechanisms. Learners will explore how to enhance chatbot accuracy, manage conversational context, and implement ethical moderation strategies. The content provides practical insights into using tools like LangChain for real-world chatbot development.
Developing Software with Generative AI
- This module explores the application of generative AI, particularly large language models (LLMs), in software development. Learners will gain hands-on experience with tools like LangChain and understand how to integrate AI for code generation and automation. The content covers evaluating model performance and implementing AI-driven workflows.
LLMs for Data Science
- This module explores the integration of large language models (LLMs) into data science workflows, focusing on automation techniques such as AutoML, agent-based systems, and natural language-driven data exploration. Learners will gain insights into how LLMs enhance efficiency and accessibility in data analysis.
Customizing LLMs and Their Output
- This module covers techniques to enhance the performance and reliability of large language models (LLMs) through methods like fine-tuning, prompt engineering, and inference-time conditioning. Learners will explore best practices for adapting models to specific tasks and improving output accuracy. The module also introduces considerations when working with open-source models and their practical applications.
Generative AI in Production
- This module covers the essential practices for deploying, evaluating, and monitoring large language models (LLMs) in production environments. Learners will gain hands-on knowledge of tools like LangSmith, FastAPI, and Ray, and learn how to ensure reliability, scalability, and transparency in generative AI applications.
The Future of Generative Models
- This module explores the future of generative models by examining technical advancements, economic impacts, and societal implications. Learners will gain insights into how AI is reshaping industries, the challenges of scaling AI, and the role of both large tech companies and smaller enterprises. It also covers the potential of AI in education and the broader cultural shifts in content creation.

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