Generative AI Apps with ChatGPT, LangChain & Hugging Face from Udemy

Unlock the power of Generative AI and learn how to build real-world applications using cutting-edge tools like ChatGPT, LangChain, Hugging Face, and more — even if you’re not a developer.

This course starts with a fast-track module for non-coders, introducing you to practical no-code AI tools like Zapier, Canva AI, and Notion AI. You’ll quickly understand how Generative AI works — no math, no jargon, just clear and practical insights.

You’ll then dive deep into Large Language Models (LLMs), learning how models like GPT and open-source alternatives function, and how to interact with them through effective prompt engineering. Understand the difference between OpenAI's APIs, local models, and when to use each.

The course progresses with hands-on projects using the OpenAI API and LangChain to build intelligent assistants, custom chatbots, and agent-based tools. You’ll explore how to integrate tools and functions, use LangGraph for complex multi-step workflows, and build applications like weather and calculator agents.

You'll also learn how to incorporate Hugging Face models, perform text classification, and explore LoRA fine-tuning basics — all with step-by-step guidance. The Retrieval-Augmented Generation (RAG) section will teach you how to connect AI with custom documents, PDFs, and websites using embeddings and vector databases like Pinecone, ChromaDB, and FAISS.

We’ll also cover critical topics like AI safety, bias, responsible prompt engineering, and deploying your apps using tools like Streamlit, Gradio, and Hugging Face Spaces. You’ll even learn how to add a simple frontend with HTML/CSS/JS to showcase your work live.

By the end of the course, you’ll complete real-world capstone projects such as a Social Media Post Generator and a Podcast AI Summarizer, and learn how to build a portfolio on GitHub that demonstrates your skills to potential clients or employers.

Whether you're a developer, freelancer, entrepreneur, or aspiring AI builder, this course will give you the skills and confidence to build intelligent applications with Generative AI.

What's inside

Learning objectives

Understand generative ai, llms, rag, and vector databases in simple terms.
Build ai apps with openai, langchain, hugging face, and vector dbs.

Use no-code ai tools like zapier, notion ai, and canva to automate tasks.
Deploy ai chatbots and apps via streamlit, gradio, and hugging face spaces.

Understand generative ai, llms, rag, and vector databases in simple terms.
Build ai apps with openai, langchain, hugging face, and vector dbs.
Use no-code ai tools like zapier, notion ai, and canva to automate tasks.
Deploy ai chatbots and apps via streamlit, gradio, and hugging face spaces.

Syllabus

No-Code AI Fast Track for Beginners

How to Use ChatGPT Like a Developer

Overview of No-Code AI Tools: Zapier

Overview of No-Code AI Tools: Notion AI

Career center

Learners who complete Generative AI Apps with ChatGPT, LangChain & Hugging Face will develop knowledge and skills that may be useful to these careers:

AI Applications Developer

An AI Applications Developer builds, integrates, and deploys intelligent software solutions utilizing artificial intelligence technologies. This course is a direct pathway to becoming an AI Applications Developer, providing comprehensive, hands-on experience in constructing real-world applications. You will learn to use cutting-edge tools like OpenAI and LangChain to create intelligent assistants and custom chatbots, and build agent-based tools with LangGraph for complex workflows. The course explicitly teaches deployment using Streamlit, Gradio, and Hugging Face Spaces, alongside adding a simple frontend with HTML/CSS/JS. Furthermore, integrating vector databases like Pinecone and ChromaDB for Retrieval Augmented Generation, along with developing capstone projects such as a Social Media Post Generator and a Podcast AI Summarizer, directly prepares you for the practical demands of this role.

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Prompt Engineer

A Prompt Engineer specializes in crafting effective instructions and queries to guide generative artificial intelligence models to produce desired outputs. This course offers a deep dive into the critical skill of strategic prompt engineering, demonstrating how to interact with large language models like GPT through clear and practical insights. Understanding the mechanics of LLMs and the differences between OpenAI's APIs and local models equips you to optimize prompts for various applications. By mastering responsible prompt engineering, you enhance both the effectiveness and ethical alignment of AI interactions. Developing intelligent assistants and custom chatbots through hands-on projects provides direct experience in applying prompt engineering principles to real-world AI application development.

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AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of complex artificial intelligence systems, ensuring they are scalable, robust, and align with business objectives. This course provides a strong foundation for an AI Solutions Architect by detailing how to integrate various cutting-edge tools and frameworks. You gain comprehensive understanding of LLM mechanics, API-based versus local deployments, and the strategic selection of models. Expertise in LangChain for building agent-based tools, integrating tools and functions, and utilizing LangGraph for multi-step workflows is crucial. Furthermore, the course's coverage of vector databases like Pinecone, ChromaDB, and FAISS for Retrieval Augmented Generation, along with deployment strategies using Streamlit, Gradio, and Hugging Face Spaces, offers the holistic view needed to architect sophisticated generative AI applications.

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AI Consultant

An AI Consultant advises organizations on leveraging artificial intelligence technologies to achieve their strategic goals, from identifying opportunities to overseeing implementation. For an aspiring AI Consultant, this course is invaluable as it provides a comprehensive overview of generative AI, LLMs, RAG, and vector databases in simple terms, without jargon. You learn to build AI apps with OpenAI, LangChain, and Hugging Face, understanding both the practical application and deployment of AI solutions. The ability to use no-code AI tools like Zapier, Notion AI, and Canva AI also helps you advise clients on quick wins and automation strategies. This broad, practical exposure allows you to speak confidently about the capabilities, limitations, and responsible use of generative AI, facilitating informed recommendations to diverse business clients.

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Automation Specialist using AI

An Automation Specialist using AI designs and implements systems that automate routine tasks and complex workflows using artificial intelligence tools. This course is particularly well-suited for an Automation Specialist using AI, as it starts with a fast-track module for non-coders, introducing practical no-code AI tools such as Zapier, Canva AI, and Notion AI. Beyond no-code solutions, you dive deep into building intelligent assistants and custom chatbots with LangChain, and even create agent-based tools like a Weather Tool Agent or a Calculator Agent for multi-step workflows using LangGraph. These skills enable you to identify opportunities for automation and develop bespoke generative AI applications that streamline operations and enhance efficiency across various business functions.

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Startup Founder AI Focus

A Startup Founder AI Focus leads the creation and growth of a new venture centered around artificial intelligence products or services. This course is an ideal resource for an aspiring Startup Founder AI Focus, offering the entrepreneurial toolkit needed to bring innovative ideas to life. It provides the skills and confidence to build intelligent applications with Generative AI, from understanding core concepts like LLMs and RAG to hands-on development with OpenAI, LangChain, and Hugging Face. The ability to deploy applications with tools like Streamlit and Gradio, add a simple frontend, and complete real-world capstone projects demonstrates practical product development. Crucially, learning how to build a portfolio on GitHub directly supports showcasing your skills to potential clients or investors, a vital step for any founder.

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Technical Trainer AI

A Technical Trainer AI educates individuals or teams on the practical application, development, and deployment of artificial intelligence technologies. This course provides an excellent foundation for a Technical Trainer AI, as it offers a step-by-step, hands-on approach to building generative AI applications. The comprehensive nature, covering everything from no-code tools for beginners to advanced topics like LangChain agents, Hugging Face models, RAG, and deployment via Streamlit and Gradio, mirrors the structured learning environment a trainer creates. The course's focus on clear, practical insights without jargon, coupled with real-world capstone projects and building a GitHub portfolio, equips you with both the knowledge and the demonstrable projects needed to effectively teach others how to master and showcase generative AI skills.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and maintains machine learning systems, often focusing on deployment and integration into production environments. While this course centers on generative AI applications, it provides a highly relevant skill set for a Machine Learning Engineer. You gain a strong understanding of Large Language Models, their mechanics, and transformer design. The course explores Hugging Face models, including text classification and LoRA fine-tuning basics, which are key aspects of modern ML development. Furthermore, learning to integrate OpenAI APIs, build agent-based tools with LangChain, and deploy applications using Streamlit, Gradio, and Hugging Face Spaces directly translates to the practical responsibilities of an ML Engineer working with cutting-edge AI models.

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Data Scientist (Natural Language Processing)

A Data Scientist Natural Language Processing analyzes and interprets complex text data using computational methods, often developing models for understanding human language. While this role typically requires an advanced degree, this course provides a robust practical foundation for a Data Scientist Natural Language Processing. You gain a deep understanding of Large Language Models, their functionality, and how to interact with them through effective prompt engineering. The course specifically covers text classification and summarization using Hugging Face models, and extensively explores Retrieval Augmented Generation (RAG) for connecting AI with custom documents, PDFs, and websites using embeddings and vector databases. These hands-on skills in working with textual data and advanced generative models are directly applicable to NLP-focused data science initiatives.

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Content Creator AI Tools

A Content Creator AI Tools leverages artificial intelligence to generate, enhance, or manage various forms of digital content, from text to multimedia. This course is exceptionally relevant for a Content Creator AI Tools, as it directly teaches how to build and utilize generative AI for content creation. You learn to use practical no-code AI tools like Canva AI and Notion AI for quick content generation. More importantly, the course culminates in capstone projects such as building an AI Social Media Post Generator and an AI Podcast Summarizer, providing direct, hands-on experience in creating custom AI tools for specific content needs. This empowers you to innovate content workflows, generate diverse material efficiently, and stay at the forefront of AI-driven content production methods.

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AI Product Manager

An AI Product Manager guides the development and strategy of AI-powered products, bridging the gap between technical teams and market needs. This course is highly beneficial for an AI Product Manager, offering a practical understanding of generative AI's capabilities and implementation details. You gain clear insights into how generative AI works, the mechanics of LLMs, and the functional differences between API-based and local deployments. Understanding tools like LangChain for building intelligent assistants and Hugging Face for model integration helps you define product scope and features. Crucially, the course's coverage of AI safety, bias, and responsible prompt engineering equips you with the knowledge to navigate ethical considerations, ensuring the development of responsible and impactful AI products.

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Technical Writer AI Products

A Technical Writer AI Products creates clear, concise documentation for complex artificial intelligence software, APIs, and applications, making technical information accessible. This course may be useful for a Technical Writer AI Products by providing a deep, practical understanding of the underlying technologies and applications. You gain firsthand experience with the tools that need documenting, including OpenAI APIs, LangChain components like Chains, Prompts, Memory, and Tools, and Hugging Face models. Understanding Retrieval Augmented Generation, vector databases, and deployment mechanisms like Streamlit and Gradio enables you to articulate how these systems function and how users interact with them. This direct exposure to building and deploying generative AI apps helps a technical writer produce accurate and comprehensible user guides and API documentation.

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Business Analyst AI Focus

A Business Analyst AI Focus identifies business needs and translates them into requirements for artificial intelligence solutions, assessing their potential impact and value. This course may be helpful for a Business Analyst AI Focus by providing a practical, non-jargon understanding of generative AI, LLMs, RAG, and vector databases. You learn about industry use cases for chatbots, visuals, and code, and gain insight into how no-code AI tools like Zapier, Notion AI, and Canva AI can automate tasks. Understanding the process of building intelligent assistants and custom chatbots with LangChain, and deploying apps, empowers you to confidently analyze how these technologies can solve real-world business problems and communicate effectively with technical development teams about generative AI capabilities.

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AI Ethicist and Policy Advisor

An AI Ethicist and Policy Advisor guides organizations in the responsible development and deployment of artificial intelligence, focusing on fairness, privacy, and societal impact. This role typically requires an advanced degree. This course may be helpful for an AI Ethicist and Policy Advisor by providing crucial practical insight into the technical underpinnings of generative AI. Explicit coverage of critical topics like AI safety, bias, and responsible prompt engineering within the course directly addresses core concerns for this field. Understanding how LLMs function, the implications of various model deployments, and the necessity of careful prompt design enables a more informed analysis of potential risks and aids in developing robust ethical guidelines and policy frameworks for AI systems.

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UX Designer AI Interfaces

A UX Designer AI Interfaces focuses on creating intuitive and effective user experiences for applications powered by artificial intelligence. While this course is not a dedicated UX curriculum, it may be useful for a UX Designer AI Interfaces by providing practical insight into how generative AI applications are constructed and deployed. You learn to build user interfaces with Streamlit and Gradio and are taught to add a simple frontend with HTML, CSS, and JavaScript to showcase your work live. This hands-on experience in bringing AI applications to users directly informs the design process, helping you understand the technical constraints, interaction possibilities, and deployment considerations critical to designing user-centric AI experiences, especially for tools involving prompt engineering and agent interactions.

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