Building Conversational Generative AI Apps with Langchain and GPT

Transform Text into Intelligent Conversations with LangChain and GPT.

Key Features
● Build AI Chatbots with LangChain, Python and GPT models through hands-on guidance.
● Master Advanced Techniques like RAG, document embedding, and LLM fine-tuning.
● Deploy and Scale conversational AI systems for real-world applications.

Book Description
Conversational AI Apps are revolutionizing the way we interact with technology, enabling businesses and developers to create smarter, more intuitive applications that engage users in natural, meaningful ways. Building Conversational Generative AI Apps with LangChain and GPT is your ultimate guide to mastering AI-driven conversational systems.

Starting with core concepts of generative AI and LLMs, you'll learn to build intelligent chatbots and virtual assistants, while exploring techniques like fine-tuning LLMs, retrieval-augmented generation (RAG), and document embedding.

As you progress, you'll dive deeper into advanced topics such as vector databases and multimodal capabilities, enabling you to create highly accurate, context-aware AI agents. The book's step-by-step tutorials ensure that you develop practical skills in deploying and optimizing scalable conversational AI solutions.

By the end, you'll be equipped to build AI apps that enhance customer engagement, automate workflows, and scale seamlessly.

Unlock the potential of Building Conversational Generative AI Apps with LangChain and GPT and create next-gen AI applications today!

What you will learn
● Build and deploy AI-driven chatbots using LangChain and GPT models.
● Implement advanced techniques like retrieval-augmented generation (RAG) for smarter responses.
● Fine-tune LLMs for domain-specific conversational AI applications.
● Leverage vector databases for efficient knowledge retrieval and enhanced chatbot performance.
● Explore multimodal capabilities and document embedding for better context-aware responses.
● Optimize and scale conversational AI systems for large-scale deployments.

Who is this book for?
This book is for developers, data scientists, and AI enthusiasts eager to build conversational applications using LangChain and GPT models. While a basic understanding of Python and machine learning concepts is beneficial, the book offers step-by-step guidance, making it accessible to both beginners and experienced practitioners.

1. Introduction to Conversational Generative AI
2. Natural Language Processing (NLP) Fundamentals
3. The Building Blocks of Rule-Based Chatbots
4. Statistical Language Models for Text Generation
5. Neural Network Architectures for Conversation
6. The Transformer Architecture Revolution
7. Unveiling ChatGPT and Architectures
8. Langchain Framework for Building Conversational AI
9. Exploring the LLM Landscape beyond GPT
10. The Transformative Impact of Conversational AI
11. Challenges and Opportunities in LLM Development
Index

Mugesh S. is an AI developer at LTIMindtree with a strong passion for leveraging data-driven insights to solve complex challenges and drive business innovation. Building on his engineering background, he completed a postgraduate program in Data Science and Engineering, along with a Master’s degree in Mathematics focused on Data Science. His expertise spans across Python programming, machine learning, and artificial intelligence, with a deep understanding of both theoretical foundations and practical implementations.

With over 8 years of hands-on experience, he has worked extensively on time series forecasting, optical character recognition (OCR), computer vision, natural language processing (NLP), and large-scale SQL/NoSQL projects. His specialization in Generative AI (Gen AI) and Large Language Models (LLMs) has led him to develop innovative AI solutions that enhance business efficiency and automate complex processes. He is also proficient in cloud computing platforms such as Microsoft Azure and Google Cloud (GCP), and has experience with version control systems such as GitLab and GitHub.

Highly passionate about advancing the field of Generative AI, he has contributed to multiple LLM-based projects, including chatbots, retrieval-augmented generation (RAG) models, and AI-driven automation tools. His strong work ethic and collaborative mindset make him an influential team player, dedicated to building scalable, high-impact AI applications that bridge the gap between cutting-edge research and real-world implementation.

ABOUT TECHNICAL REVIEWER

Rajesh Mane is a seasoned AI specialist with over 7 years of experience in developing and implementing cutting-edge AI solutions. He holds a Master’s degree in Electrical Engineering from the College of Engineering Pune (COEP) and has contributed to the research community with publications in IEEE. His expertise spans across traditional machine learning, natural language processing (NLP), computer vision, and Generative AI, with a focus on building scalable AI-driven applications and architecting data-intensive systems.

Rajesh has worked across various industries, including finance, retail, telecommunications, and SaaS, applying AI to solve complex business challenges. His work involves designing and optimizing intelligent systems, automating workflows, and integrating AI into enterprise applications. Passionate about AI-driven transformation, he actively collaborates with cross-functional teams to bridge the gap between research and real-world Implementation.

Currently, Rajesh is focused on advancing AI-driven automation and chatbot development, helping businesses leverage AI and Generative AI technologies for enhanced efficiency and decision-making. He is dedicated to pushing the boundaries of AI and enabling businesses to harness its full potential. His commitment to applied research and real-world AI adoption makes him a key contributor in the evolving AI landscape.