2025 Bootcamp: Generative AI, LLM Apps, AI Agents, Cursor AI from Udemy

This Online Bootcamp is a compact and accelerated version of our 400-hour in-person master's program.

It has four parts:

- In Part 1, you will learn the keys to Artificial Intelligence and the new Generative AI, as well as its potential to revolutionize businesses, startups, and employment.

- In Part 2, you will learn to build professional-level LLM Applications, the most potential applications of Generative AI. You will also learn how to build Advanced RAG LLM Apps, Multimodal LLM Apps, AI Agents, Multi-Agent LLM Apps, and how to manage LLMOps.

- In Part 3, you will learn how to build traditional and Gen AI apps without coding using Cursor AI and the new AI Coding Assistants. You will learn what are AI Coding Assistants like Cursor AI, Claude AI, v0, o1, Replit Agent, etc, and how to increase their performance by combining them with tools like the Replit platform, simplified backends like Firebase, Replicate AI, Stable Fusion, or Deepgram.

- In Part 4, you will learn how to create SaaS applications without coding using Cursor AI. You’ll also see, through two high-level real-world examples, how Generative AI is transforming the SaaS (Software as a Service) model.

By the end of this program, you will know how to do the following:

Know the new opportunities created by AI for businesses.

Design a plan to introduce AI into your company.

Select an appropriate pilot project to introduce AI into your company.

Form the first AI team in your company.

Prepare your company's AI strategy.

Know the new professions created by AI.

Know the main use cases of LLM Applications in businesses and startups.
RAG LLM Applications.
Multimodal LLM Applications.
AI Agents.
Multi-Agent LLM Applications.

You will learn the Architecture of an LLM Application.
You will learn how to learn programming languages like Python and Javascript.
You will learn to work with your computer's terminal.
You will learn to work with Jupyter notebooks.
You will learn to work with code editors like Visual Studio Code.
You will learn to work with virtual environments.
You will learn to work with hidden files to save credentials.
You will learn the RAG (Retrieval Augmented Generation) technique.
You will learn to use LangChain.
You will learn to use the LangChain Expression Language (LCEL).
You will learn LCEL in depth.
You will learn to use the new versions v010 and v020 of LangChain.
You will learn to use LlamaIndex.
You will learn to use the OpenAI API.
You will learn to use OpenAI's functions.
You will learn to use LangSmith.
You will learn to use LangServe.
You will learn to use templates of LangChain and LlamaIndex.
You will learn what AI Agents are and how to create them.
You will learn to create prototypes (demos) of LLM applications with LangChain and Streamlit.
You will learn to create full-stack CRUD applications with Nextjs, FastAPI, and Postgres.
You will learn to create professional full-stack LLM applications with LangChain, LlamaIndex, Nextjs, Tailwind CSS, FastAPI, Flask, and Postgres.
You will learn to use vector and traditional databases.
You will learn to deploy applications on Vercel and Render.
You will learn to use AWS S3 as a remote storage platform.
You will learn to use ChatGPT as a programming assistant.
You will learn to use GPT4-Vision and GPT4o.
You will learn to work with Github and Github Codespaces.
You will learn what LLMOps is and how to use it in your LLM Applications.
You will learn the principles of Responsible AI and how to use them in your LLM Applications.
You will learn how to build advanced RAG LLM Applications.
You will learn how to build the new Multimodal LLM Applications.
You will learn how to build the new AI Agents.
You will learn how to build the new Multi-Agent LLM Applications.
You will learn to use LangGraph.
You will learn to use CrewAI.

Analysis of Cursor AI and the top AI Coding Assistants.

Top strategies and techniques to get the most from Cursor AI.

The best Cursor AI combo for beginners: custom starter template, Replit, v0, and Firebase.

How to build 6 complete projects without coding using Cursor AI: from a simple to-do list app, to a social network, a chatbot, a tex-to-image app, a voice-to-text app, and a basic full-stack SaaS app with authentication and payment systems.

How AI Agents are killing SaaS apps.

The future of SaaS and Micro SaaS.

The Bootcamp consists of:

More than 650 lessons divided into sections.
More than 650 videos.
More than 320 attached presentations.
More than 220 practical notebooks.
50 practical code repositories on Github.
45 LLM applications of different difficulty levels: basic, intermediate, and advanced.
Material for more than 400 hours of study and practice for the student.
2 downloadable books valued at $50: "Keys to Artificial Intelligence" and "100 AI Startups that made more than $500,000 before the first year".

Topics included in this Bootcamp:

AI, Generative AI, AI Applications, LLM Applications, Multimodal LLM Applications, chatGPT, Llama2, GPT-4 Vision, GPT4o, Full-Stack Applications, LangChain, LangChain Expression Language (LCEL), LangChain v010, LangChain v020, LlamaIndex, OpenAI, Open5 Sonnet, o1, o1-preview, o1-mini, Replit Agent, Replit, Firebase, Supabase, Replicate AI, Stable Fusion, Deepgram, SaaS, Micro SaaS.

What's inside

Learning objectives

Keys to ai, generative ai, llm apps, and new ai coding assistants like cursor ai.
Llm apps with langchain, crewai, langgraph, langserve and langsmith.
How to build apps without coding using cursor ai and ai coding assistants.
How to build the new multimodal and multi-agent llm applications.
Opportunities and threats of ai for businesses, startups, and jobs.
Rag applications in depth: full stack rag apps and advanced techniques.
How to manage llmops: observability, evaluation, testing, etc.
Professional opportunities opened by artificial intelligence.
Steps to become an artificial intelligence engineer.
How to introduce artificial intelligence into your business.
Keys to llm applications, the highest potential applications of generative ai.
Architecture of professional llm applications.
The rag technique (retrieval augmented generation).
Artificial intelligence agents.

Basic and advanced langchain, langchain lcel, and langchain v010. langsmith, langserve, langchain templates.
Lcel (langchain expression language) in depth.
Basic and advanced llamaindex. llamaindex templates.
Chatgpt, openai, openai functions, and the openai api.
Large language models (llm): chatgpt, llama2, mistral, falcon, etc.
Vector databases: postgres, pinecone, chroma, faiss, deeplake, etc.
Full-stack applications: nextjs and fastapi.
Professional deployment: vercel and render.
Provisional deployment: streamlit.
Cloud hosting: aws s3.
How to apply the principles of responsible ai.
Daily tools of the ai engineer: jupyter notebooks, python, terminal, github, codespaces, etc.
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Keys to ai, generative ai, llm apps, and new ai coding assistants like cursor ai.
Llm apps with langchain, crewai, langgraph, langserve and langsmith.
How to build apps without coding using cursor ai and ai coding assistants.
How to build the new multimodal and multi-agent llm applications.
Opportunities and threats of ai for businesses, startups, and jobs.
Rag applications in depth: full stack rag apps and advanced techniques.
How to manage llmops: observability, evaluation, testing, etc.
Professional opportunities opened by artificial intelligence.
Steps to become an artificial intelligence engineer.
How to introduce artificial intelligence into your business.
Keys to llm applications, the highest potential applications of generative ai.
Architecture of professional llm applications.
The rag technique (retrieval augmented generation).
Artificial intelligence agents.
Basic and advanced langchain, langchain lcel, and langchain v010. langsmith, langserve, langchain templates.
Lcel (langchain expression language) in depth.
Basic and advanced llamaindex. llamaindex templates.
Chatgpt, openai, openai functions, and the openai api.
Large language models (llm): chatgpt, llama2, mistral, falcon, etc.
Vector databases: postgres, pinecone, chroma, faiss, deeplake, etc.
Full-stack applications: nextjs and fastapi.
Professional deployment: vercel and render.
Provisional deployment: streamlit.
Cloud hosting: aws s3.
How to apply the principles of responsible ai.
Daily tools of the ai engineer: jupyter notebooks, python, terminal, github, codespaces, etc.
Show more
Show less

Syllabus

Program presentation

[NEW] Generative AI: What is happening in 2024?

[NEW] The Online Bootcamp: Learning Paths & Learning Rhythms by Student Profile

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers LLMOps, which is essential for monitoring, evaluating, and testing LLM applications, ensuring their reliability and performance in real-world scenarios

Explores LangChain and LlamaIndex, which are frameworks that simplify the development of LLM-powered applications, making it easier to build complex AI solutions

Discusses how to introduce AI into a company, including selecting pilot projects and forming AI teams, which is valuable for organizations looking to adopt AI technologies

Includes hands-on experience with tools like Jupyter Notebooks, Python, and Github, which are essential for AI engineers in their daily work and project development

Requires learners to learn programming languages like Python and Javascript, which may pose a barrier to entry for those without prior programming experience

Features GPT4-Vision and GPT4o, which are cutting-edge models that enable the development of advanced multimodal applications, providing learners with exposure to state-of-the-art AI technology

Reviews summary

Comprehensive llm app development bootcamp

According to learners, this bootcamp is a largely positive and very comprehensive program for building modern LLM applications. Students praise the practical focus, noting that the hands-on projects using tools like LangChain, LlamaIndex, Nextjs, and FastAPI are highly effective for solidifying understanding and building a portfolio. The coverage of topics like RAG, AI Agents, LLMOps, and full-stack deployment is considered up-to-date and relevant for professionals. However, learners warn that the pace is very fast and the course is best suited for those with prior coding experience, as the introductory programming sections are brief. Some also note that the rapid evolution of libraries can lead to examples needing tweaking, and the section on no-code tools (Part 3) is considered less valuable than the core LLM development content.

Covers a wide range of relevant topics.

"Very comprehensive covering LLMs, RAG, Agents, LangChain, LlamaIndex, full-stack dev, and deployment."

"Covers everything from basics to advanced topics like RAG, agents, multimodal, LLMOps, and full-stack deployment."

"Excellent course! Very comprehensive covering LLMs, RAG, Agents, LangChain, LlamaIndex, full-stack dev, and deployment."

"The amount of material is huge, definitely feels like 400 hours packed in."

Hands-on coding solidifies learning.

"The sections on LangChain and LlamaIndex are incredibly detailed and the hands-on projects truly solidify the learning."

"Building full-stack LLM apps felt daunting at first, but the step-by-step guidance, especially with Nextjs and FastAPI, made it manageable."

"The projects are very practical and help you build a portfolio."

"The practical exercises and code repositories are invaluable."

"The projects are realistic and helped me apply the concepts immediately."

Section on no-code tools less impactful.

"Part 3 on Cursor AI and no-code was interesting but felt less core to the 'bootcamp' feel than the coding parts."

"Part 3 felt a bit out of place and less detailed than the LLM development sections."

"Part 3 on Cursor AI feels like an advertisement and not deep technical content."

Examples may need tweaking due to updates.

"the frequent updates to libraries like LangChain mean some examples require tweaking to work with the absolute latest versions."

"keeping up with daily changes is hard."

"examples often don't work out of the box due to dependency issues or rapid changes in libraries like LangChain."

"specific library versions can be an issue sometimes."

Curriculum is intense and demanding.

"The pace is fast, which is expected, but sometimes the material assumes a bit more background than stated."

"Good, but intense. The amount of material is huge..."

"The pace is very fast, and if you don't have a strong coding background... you might struggle."

"The curriculum is ambitious, and you need to dedicate significant time."

Not ideal for complete beginners.

"The pace is fast... sometimes the material assumes a bit more background than stated."

"if you don't have a strong coding background (Python, JS), you might struggle."

"The sections on setting up environments and basic programming tools are too brief for beginners."

"Definitely not for beginners."

"Best suited for those with some prior coding experience."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 2025 Bootcamp: Generative AI, LLM Apps, AI Agents, Cursor AI with these activities:

Review Python Fundamentals

Show steps

Strengthen your understanding of Python, a core language used in LLM app development, to ensure a smoother learning experience during the course.

Browse courses on Python Programming

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Complete online Python tutorials.
Practice writing basic Python scripts.
Review data structures and algorithms in Python.

Brush Up on Key AI Concepts

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Revisit fundamental AI concepts to better grasp the advanced topics covered in the bootcamp, such as LLM applications and AI agents.

Browse courses on Artificial Intelligence

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Read articles on AI and machine learning.
Watch introductory videos on AI concepts.
Review the history and evolution of AI.

Read 'Building Applications with Large Language Models'

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Gain practical insights into building LLM applications by reading this comprehensive guide, which complements the bootcamp's hands-on approach.

View Putting Knowledge to Work on Amazon

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Read the book chapter by chapter.
Experiment with the code examples provided.
Take notes on key concepts and techniques.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Build a Simple Chatbot with LangChain

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Apply your knowledge of LangChain by building a basic chatbot, reinforcing your understanding of LLM application development.

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Set up a development environment with LangChain.
Design the chatbot's functionality and features.
Implement the chatbot using LangChain components.
Test and refine the chatbot's performance.

Document Your LLM App Development Journey

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Solidify your learning by documenting your experiences, challenges, and solutions while building LLM applications, creating a valuable resource for yourself and others.

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Choose a platform for documenting your journey.
Regularly update your documentation with new insights.
Share your documentation with the community.

Explore 'Generative AI with LangChain'

Show steps

Deepen your understanding of LangChain and generative AI by exploring this advanced guide, which expands on the bootcamp's coverage of these topics.

View Putting Knowledge to Work on Amazon

Show steps

Read the book and take detailed notes.
Implement the advanced techniques discussed.
Experiment with different LangChain components.

Contribute to a LangChain Project

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Enhance your skills and contribute to the community by participating in open-source LangChain projects, gaining valuable experience and insights.

Show steps

Identify a LangChain project to contribute to.
Familiarize yourself with the project's codebase.
Contribute bug fixes, documentation, or new features.

Career center

Learners who complete 2025 Bootcamp: Generative AI, LLM Apps, AI Agents, Cursor AI will develop knowledge and skills that may be useful to these careers:

LLM Application Developer

An LLM Application Developer specializes in building applications powered by Large Language Models. This course is extremely relevant for an LLM Application Developer because it provides in depth training in this field. The course has multiple sections focused on building advanced LLM applications that use RAG, are multimodal, and leverage AI Agents, which teaches the exact skills needed to become a successful LLM Application Developer. Additionally, the course covers LLMOps and responsible AI principles, which are critical for deploying safe and effective LLM applications.

See salaries and explore the career path for LLM Application Developer

Generative AI Specialist

A Generative AI Specialist is an expert in using generative AI models and tools. This course provides a complete introduction to the field and is well-suited for a Generative AI Specialist. The curriculum covers everything from understanding the basics of generative AI to actually developing and deploying advanced LLM applications. The many practice elements, such as practical notebooks, code repositories, and LLM applications, give a firm command of concepts. The course is also uniquely useful because it covers not only the use of models such as ChatGPT, but also the development of novel applications using tools such as Langchain and LlamaIndex.

See salaries and explore the career path for Generative AI Specialist

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and deploys AI models and applications. This course is particularly helpful for aspiring AI Engineers because it covers key aspects, from understanding the fundamentals of AI and Generative AI to building professional-level LLM applications, including RAG, multimodal, and AI agent-based systems. The course goes further by teaching students to use tools that are useful for an Artificial Intelligence Engineer, such as Langchain, LlamaIndex, and the OpenAI API. It also covers important aspects of LLMOps and responsible AI practices.

See salaries and explore the career path for Artificial Intelligence Engineer

AI Application Developer

An AI Application Developer focuses on creating the front end of applications that use artificial intelligence. This course is perfect for an AI Application Developer since it teaches how to build various types of LLM apps, including RAG, multimodal, and AI Agent-based applications. It also teaches how to use tools like LangChain, LlamaIndex and the OpenAI API. Moreover, the course covers using AI coding assistants to build applications, including full stack applications. It is geared toward building a variety of applications and SaaS models, which are cornerstones of a career as an AI Application Developer.

See salaries and explore the career path for AI Application Developer

Machine Learning Engineer

A Machine Learning Engineer builds and implements machine learning models. This course is useful for a Machine Learning Engineer because it provides an understanding of generative AI and large language models, which are increasingly important in machine learning. Moreover, the practical experience of building LLM applications, including RAG and multimodal systems, together with exposure to technologies such as LangChain and LlamaIndex, prepares students to implement sophisticated machine learning solutions. The course also introduces the use of AI coding assistants like Cursor AI, which are increasingly important in the field of Machine Learning.

See salaries and explore the career path for Machine Learning Engineer

SaaS Developer

A SaaS Developer builds software as a service applications. This course is suitable for a SaaS Developer, and it gives an in depth discussion on how generative AI is changing the SaaS model and the future of SaaS, and gives practical instruction on how to build SaaS applications using AI coding assistants. It provides a complete introduction into how AI is changing the world of software, and goes on to teach how to build both full-stack and no-code applications using AI. It also covers AI agents and their role in the new world of SaaS.

See salaries and explore the career path for SaaS Developer

AI Consultant

An AI Consultant advises businesses on how to implement AI solutions. This course provides a good foundation for an aspiring AI Consultant, because a big emphasis is given to how to introduce AI into a company, how to form an AI team, and how to prepare an AI strategy for a firm. The course also provides a deep dive into the use cases of LLM applications in businesses and startups, which are crucial for any consultant working with AI. Exposure to building and deploying LLM applications gives a consultant the hands-on experience needed to understand client needs.

See salaries and explore the career path for AI Consultant

Full-Stack Developer

A Full Stack Developer creates both the front-end and back-end of web applications. This course is relevant for a Full Stack Developer because it includes instruction on how to build full-stack LLM applications using technologies such as Nextjs, FastAPI and Postgres. It also includes lessons on how to deploy full-stack applications on platforms such as Vercel and Render. The course also teaches how to build with AI coding assistants which may help boost a full stack professional's productivity. The course teaches to build with a host of relevant technologies, including Tailwind CSS and Flask.

See salaries and explore the career path for Full-Stack Developer

Software Developer

A Software Developer creates applications and systems. This course may be useful for a Software Developer looking to integrate generative AI capabilities into their work. It provides instruction in building applications using LLMs, including RAG and multimodal systems, and is designed to teach how to leverage tools such as LangChain, LlamaIndex and the OpenAI API. Furthermore, the course covers full-stack application development with Nextjs, FastAPI and Postgres, which is a useful skill for developing complex software projects. The course also teaches AI coding assistants such as Cursor AI, which will streamline coding workflows.

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Solutions Architect

A Solutions Architect designs technical solutions to business problems. This course provides useful background for a Solutions Architect because it provides and overview of AI, a deep dive into LLM applications, and instruction on how to introduce AI into your company. Moreover, the course also teaches practical skills like full stack app development using systems and technologies such as Vercel and Render. The course also talks about how AI is disrupting SaaS applications, which will be useful to any Solutions Architect working in that area.

See salaries and explore the career path for Solutions Architect

Data Scientist

A Data Scientist analyzes data to extract insights and inform decisions. This course may be helpful for a Data Scientist interested in integrating generative AI into their work. This course introduces students to the basics of AI and generative AI, then moves into the use of LLMs and the creation of applications. This knowledge can be useful for Data Scientists who are looking to leverage the power of generative models in their analysis. Furthermore, the course covers skills that are adjacent to Data Science, such as working with the terminal, with virtual environments, and with code editors.

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AI Product Manager

An AI Product Manager defines the vision and strategy for AI products. This course may be useful for an AI Product Manager because it provides a good understanding of the potential of AI, particularly generative AI and LLM applications. The course begins by discussing the potential of AI to disrupt business and startups and goes on to discuss the technical aspects of building LLM applications and integrating them into products. An AI Product Manager should have knowledge of all these topics, as this will make them better able to understand the possibilities.

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Research Scientist

A Research Scientist conducts research to advance scientific knowledge. This course may be useful for a Research Scientist working in AI. The course covers many advanced topics in AI such as AI Agents, multimodal applications, and LLMOps. This may give a research scientist a good overview of the state of the field and may give rise to ideas for novel experiments. Moreover, the course introduces the foundational technologies such as LangChain and LlamaIndex, which may be useful for research into new algorithms or techniques.

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Robotics Engineer

A Robotics Engineer designs, builds, and programs robots. This course may be useful for a Robotics Engineer because the course's in depth discussion of AI agents, multi agent LLM applications, and the use of multimodal LLM applications can be useful for robotics engineers who are increasingly using AI. The course also teaches how to use AI coding assistants, and also provides instruction on working with code, with Github, and with codespaces, which are all adjacent and useful skills.

See salaries and explore the career path for Robotics Engineer

Data Engineer

A Data Engineer designs and builds the infrastructure for data systems. This course may be useful to a Data Engineer, because some sections are adjacent to the Data Engineering field. For instance, the course covers how to work with vector and traditional databases, which is a useful skill to have for a data engineer. The course also covers how to work with code, with the terminal, and with virtual environments, all of which are skills that are useful in the Data Engineering field. The course may be useful to a data engineer who wishes to move into the generative AI field.

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2025 Bootcamp

Generative AI, LLM Apps, AI Agents, Cursor AI

What's inside

Learning objectives

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Reviews summary

Comprehensive llm app development bootcamp

Activities

Career center

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