AI Automation: Build LLM Apps & AI-Agents with n8n & APIs from Udemy

AI Automation is the Future.

But how does it really work? And how can AI optimize business processes—on a whole new level, far beyond ChatGPT? The answer: AI Agents.

This course guides you through both essential and advanced concepts in automation using AI automation, AI agents, LLMs, vector databases, Retrieval-Augmented Generation (RAG), and n8n. You'll learn how to create powerful automations, build intelligent AI agents, and seamlessly integrate them into your workflows to enhance both business and personal projects.

Additionally, you'll receive 29 downloadable JSON workflows to accelerate your learning and implementation.

What You’ll Learn in This Course:

Fundamentals of Automation, AI Agents & LLMs

Dive into the world of AI automation:

Introduction to automation, AI agents & essential tools (n8n, Make, Zapier, LangChain, LangGraph, Flowise).
Understanding APIs and their role in automation.
LLMs explained: ChatGPT, Claude, Gemini, Deepseek, Llama, Mistral & more.
OpenAI API: Pricing structure, GDPR-compliant usage & project setup.
Function calling with LLMs: How AI agents use tools like calendars, emails, web search, webhooks, Airtable, Google Sheets, and more.
RAG (Retrieval-Augmented Generation): Vector databases & embeddings explained.

n8n Basics: Installation & First Workflows

Master the fundamentals of n8n, the key to intelligent automation:

Local installation with Node.js & using the web version without installation.
Importing, exporting, and selling workflows.
Setting up automations with Airtable, Google Sheets & Google Cloud.
Using simple JavaScript variables in automation.

Expanding AI Automation with LLMs

Build advanced AI-powered automations:

Email automation with OpenAI API, Gmail, and Airtable.
Real-time sentiment analysis & database storage.
Integrating open-source LLMs (Deepseek R1, Llama, Mistral) into automation.
Using any LLM API in n8n (Deepseek API, Groq API & more).

Integrating AI Agents & RAG Chatbots into Workflows

Automate customer communication & data processing:

RAG Agent: Automatically updating vector databases with Google Drive.
RAG Chatbot using AI agent nodes, embeddings & retrieval techniques.
AI-powered email agents for automated summaries & responses.

Prompt Engineering for AI Agents

Optimize your prompts for better AI responses:

Principles & best practices for effective prompt engineering.
Avoiding errors & precisely controlling AI outputs.

Hosting, Social Media & Advanced Automations

Expand your automations with self-hosting & real-time integrations:

n8n self-hosting with Render & other options.
Using AI agents in WhatsApp & Telegram.
Social media automation with sub-workflows, webhooks & web scraping.

Debugging & Optimizing API Integrations

Enhance performance & error handling in n8n workflows:

Debugging strategies for error-free n8n automations.
Connecting Flowise AI agents with webhooks & Google Sheets.
Extending n8n with Flowise & JavaScript custom tools.

Building a Business with AI Automation & AI Agents

Leverage your skills to create a profitable AI automation business:

Selling automations & AI agents as services.
Developing market-ready RAG bots for lead generation & website integration.
Marketing strategies for successfully selling AI solutions.

Optimizing RAG Chatbots: Data Quality & Chunking

Improve AI responses with optimized data strategies:

Chunk size, overlap & data quality for better chatbot performance.
Using Firecrawl for web data extraction in Markdown format.
LlamaIndex & LlamaParse for data preprocessing in Google Colab.

Security, Privacy & Ethical Considerations

Protect your AI agents & ensure GDPR compliance:

Understanding & preventing jailbreaks, prompt injections & data poisoning.
Ensuring copyright & data protection for AI-generated content.
Key legal frameworks: EU AI Act & more

Additionally, you'll gain access to 29 ready-to-use JSON workflows, available for download to streamline your learning experience and accelerate implementation.

Become an Expert in AI Agents & Automation.

After this course, you will have a deep understanding of AI automation, n8n, LLMs & RAG and be able to develop, optimize, and deploy powerful AI agents for business applications.

What's inside

Learning objectives

Fundamentals of automation, ai agents & llms (chatgpt, claude, gemini, deepseek, llama, mistral & more)
Introduction to automation & key tools (n8n, make, zapier, langchain, langgraph, flowise)
Understanding and utilizing apis for automation
Openai api: pricing structure, compliant usage & project setup
Function calling with llms: using calendars, emails, web search, webhooks, airtable, google sheets & more
Everything about vector databases, embedding models & retrieval-augmented generation (rag)
N8n basics & automation applications
Basics of n8n: installation, importing, exporting & selling workflows
Automations with airtable, google sheets & google cloud
Using simple javascript variables in automation
Expanding ai automation with llms: email automation, sentiment analysis, databases
Integrating open-source llms (deepseek r1, llama, mistral) into automation
Using external llm apis in n8n (deepseek api, groq api & more)
Ai agents & rag chatbots in workflows
Integrating ai agents & rag chatbots into workflows
Automated vector database updates with google drive
Rag chatbot with ai agent node, embeddings & retrieval techniques
Ai-powered email agents for automated summaries & responses
Prompt engineering: principles, best practices & avoiding errors

Hosting, social media & advanced automations
N8n self-hosting with render & other options
Using ai agents in whatsapp, telegram & social media
Web scraping & automation with sub-workflows & webhooks
Debugging strategies for error-free n8n automation
Connecting flowise ai agents with webhooks & google sheets
Extending n8n with flowise & javascript custom tools
Business & market aspects of ai automation
Ai automation as a business: selling automation & ai agents
Creating market-ready rag bots for lead generation & website integration
Marketing strategies for successfully selling ai solutions
Optimizing rag chatbots: chunk size, overlap & data quality
Llamaindex & llamaparse for data preprocessing in google colab
Using firecrawl for web data extraction in markdown format
Security, privacy & ethical concerns: jailbreaks, prompt injections & data poisoning
Copyright & data protection for ai-generated data
Legal frameworks: eu ai act & more
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Fundamentals of automation, ai agents & llms (chatgpt, claude, gemini, deepseek, llama, mistral & more)
Introduction to automation & key tools (n8n, make, zapier, langchain, langgraph, flowise)
Understanding and utilizing apis for automation
Openai api: pricing structure, compliant usage & project setup
Function calling with llms: using calendars, emails, web search, webhooks, airtable, google sheets & more
Everything about vector databases, embedding models & retrieval-augmented generation (rag)
N8n basics & automation applications
Basics of n8n: installation, importing, exporting & selling workflows
Automations with airtable, google sheets & google cloud
Using simple javascript variables in automation
Expanding ai automation with llms: email automation, sentiment analysis, databases
Integrating open-source llms (deepseek r1, llama, mistral) into automation
Using external llm apis in n8n (deepseek api, groq api & more)
Ai agents & rag chatbots in workflows
Integrating ai agents & rag chatbots into workflows
Automated vector database updates with google drive
Rag chatbot with ai agent node, embeddings & retrieval techniques
Ai-powered email agents for automated summaries & responses
Prompt engineering: principles, best practices & avoiding errors
Hosting, social media & advanced automations
N8n self-hosting with render & other options
Using ai agents in whatsapp, telegram & social media
Web scraping & automation with sub-workflows & webhooks
Debugging strategies for error-free n8n automation
Connecting flowise ai agents with webhooks & google sheets
Extending n8n with flowise & javascript custom tools
Business & market aspects of ai automation
Ai automation as a business: selling automation & ai agents
Creating market-ready rag bots for lead generation & website integration
Marketing strategies for successfully selling ai solutions
Optimizing rag chatbots: chunk size, overlap & data quality
Llamaindex & llamaparse for data preprocessing in google colab
Using firecrawl for web data extraction in markdown format
Security, privacy & ethical concerns: jailbreaks, prompt injections & data poisoning
Copyright & data protection for ai-generated data
Legal frameworks: eu ai act & more
Show more
Show less

Syllabus

Introduction

Welcome!

Course Overview

Important Tips for the Course

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides downloadable JSON workflows, which can significantly accelerate the learning process and practical implementation of AI automation concepts

Explores the EU AI Act, which is essential knowledge for developers deploying AI solutions within the European Union

Covers prompt injection and data poisoning, which are critical security considerations for AI agents and automations

Teaches n8n, which is a low-code platform that allows users to connect different applications and services to automate tasks

Discusses self-hosting n8n with Render, which provides learners with options for deploying and managing their automation workflows

Requires learners to install Node.js, which may pose a barrier to entry for those unfamiliar with JavaScript development environments

Reviews summary

Practical ai automation with n8n & llms

According to learners, this course offers a practical and comprehensive guide to building AI automations and agents. Students highlight the extensive coverage of essential tools like n8n, along with various LLMs and concepts such as RAG and function calling. The hands-on approach and real-world examples are frequently praised as particularly helpful for understanding and applying the concepts. Many appreciate the inclusion of ready-to-use workflows and the focus on building an AI automation business. While generally very positively received, a few mention that the pace can be fast, suggesting some prior technical familiarity is beneficial.

Includes strategies for selling services.

"I appreciated the section on building an AI automation business and selling services."

"The focus on market-ready bots and marketing strategies is unique and helpful."

"Gave me concrete ideas on how to monetize my new AI automation skills."

Included JSON workflows are a great resource.

"The downloadable JSON workflows are incredibly useful and a great bonus."

"Saved a lot of time by starting with the provided templates for various automations."

"Having ready-to-use examples accelerated my learning and implementation significantly."

"These templates are a valuable asset for building out my own projects quickly."

Complex AI concepts are explained simply.

"Complex topics like RAG and function calling were explained simply and effectively."

"The instructor's explanations of embeddings and vector databases were particularly clear."

"Helped me understand the core principles of APIs and LLMs deeply."

"The breakdown of technical jargon made intimidating topics accessible."

Covers a wide range of relevant tools and topics.

"It covers LLMs, RAG, n8n, hosting, security, and even business strategies. Truly comprehensive."

"Great to see modern tools like n8n used, and integration with various current LLMs beyond ChatGPT."

"The course feels very up-to-date with the latest trends in AI automation and agent building."

"Includes essential concepts like vector databases and function calling explained well."

Strong focus on practical application and projects.

"The hands-on exercises using n8n and integrating various APIs were extremely valuable for learning."

"I could immediately apply the concepts learned to build my own workflows. Very practical!"

"Building real-world projects and automations helped solidify my understanding more than just theory."

"Loved the practical examples shown for sentiment analysis and email automation."

"The demos were incredibly helpful for seeing how everything connects in practice."

Minor setup or version challenges possible.

"Setting up n8n locally had some minor issues, though troubleshooting steps were provided."

"As technology moves fast, some specific tool versions required minor adjustments."

"Encountered a few small technical glitches during workflow implementation."

May require prior tech knowledge or rewatching.

"The course moves quite fast in some sections, especially if you're new to APIs or Node.js."

"Would recommend having basic familiarity with coding or automation tools beforehand."

"I needed to rewatch some lectures multiple times to fully grasp complex concepts."

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 AI Automation: Build LLM Apps & AI-Agents with n8n & APIs with these activities:

Review API Fundamentals

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Solidify your understanding of APIs, which are fundamental to integrating LLMs and other services within n8n workflows.

Browse courses on API

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Review the different types of APIs (REST, GraphQL).
Practice making API calls using tools like Postman or Insomnia.
Study API documentation for services you plan to use.

Read 'LangChain in Action'

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Gain a deeper understanding of LLMs and their application in AI by reading this book.

View Melania on Amazon

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Read the chapters related to prompt engineering and API integration.
Experiment with the code examples provided in the book.
Relate the concepts learned to the n8n workflows covered in the course.

Read 'Building AI Applications with ChatGPT and Python'

Show steps

Gain a deeper understanding of LLMs and their application in AI by reading this book.

View Melania on Amazon

Show steps

Read the chapters related to prompt engineering and API integration.
Experiment with the code examples provided in the book.
Relate the concepts learned to the n8n workflows covered in the course.

Four other activities

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Prompt Engineering Exercises

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Refine your prompt engineering skills through targeted exercises to improve the quality of AI responses.

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Select a specific task (e.g., summarizing a document, generating creative content).
Experiment with different prompt structures and techniques (e.g., few-shot learning, chain-of-thought prompting).
Evaluate the AI's responses and iterate on your prompts to achieve the desired outcome.

Build a Simple RAG Chatbot

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Apply your knowledge by building a RAG chatbot using n8n, a vector database, and an LLM.

Show steps

Set up a vector database (e.g., Pinecone, Weaviate).
Create an n8n workflow to ingest data and create embeddings.
Build a chatbot interface using a platform like Telegram or WhatsApp.
Connect the chatbot to your n8n workflow for querying the vector database.

Document Your AI Automation Projects

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Improve your understanding and share your knowledge by documenting the AI automation projects you build.

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Choose a project you've built in n8n.
Write a detailed explanation of the workflow, including the purpose, steps, and challenges.
Create diagrams or screenshots to illustrate the workflow.
Share your documentation on a blog, forum, or social media platform.

Contribute to n8n Community Workflows

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Deepen your understanding of n8n and AI automation by contributing to the n8n community.

Show steps

Explore the n8n community forum and GitHub repository.
Identify areas where you can contribute, such as creating new workflows, improving existing ones, or writing documentation.
Submit your contributions and participate in discussions with other community members.

Career center

Learners who complete AI Automation: Build LLM Apps & AI-Agents with n8n & APIs will develop knowledge and skills that may be useful to these careers:

Prompt Engineer

A Prompt Engineer designs and optimizes prompts for large language models. This course is directly applicable to this role, because it features an entire section on prompt engineering for AI agents and automations. You'll learn key principles and best practices for crafting effective prompts and avoiding errors. The course helps ensure that you can precisely control AI outputs. With this knowledge, you can ensure that AI agents provide consistent, accurate, and relevant responses, which is the primary goal of a Prompt Engineer. You will also have experience in debugging and optimizing, which is useful to the role.

See salaries and explore the career path for Prompt Engineer

Chatbot Developer

A Chatbot Developer specializes in creating and deploying conversational AI agents. This course is invaluable because it provides a thorough understanding of RAG chatbots, including AI agent nodes, embeddings, and retrieval techniques. You'll learn how to build and optimize chatbots using n8n. The course also covers prompt engineering, which is essential for creating chatbots that provide accurate and relevant responses. Furthermore, the course teaches you how to manage vector databases and automate updates, ensuring that your chatbots remain up-to-date with the latest information.

See salaries and explore the career path for Chatbot Developer

Workflow Automation Specialist

A Workflow Automation Specialist designs and implements automated workflows to optimize business processes. This course is highly relevant because it focuses on automation using n8n, a powerful workflow automation platform. You'll learn how to create intelligent workflows, integrate various APIs, and automate tasks like email handling and data processing. The course's coverage of debugging and optimizing API integrations ensures that you can build robust and efficient workflows. Also, the downloadable JSON workflows provided in this course accelerate the implementation of custom solutions.

See salaries and explore the career path for Workflow Automation Specialist

Business Process Automation Specialist

A Business Process Automation Specialist identifies and automates repetitive tasks to improve efficiency. This course is directly relevant because it focuses on AI automation using n8n, LLMs, and AI agents. You'll learn how to create powerful automations, integrate various APIs, and streamline workflows. The course's emphasis on debugging and optimizing API integrations enables you to build robust and efficient systems. Also, the ready-to-use JSON workflows provided in the course greatly accelerate the implementation of automation solutions.

See salaries and explore the career path for Business Process Automation Specialist

RAG Engineer

A RAG Engineer focuses on designing and implementing Retrieval-Augmented Generation systems. This course is highly relevant because it covers RAG in detail, explaining vector databases, embedding models, and retrieval techniques. You'll learn how to build RAG chatbots using AI agent nodes and automate vector database updates. The course also emphasizes optimizing RAG chatbots through data quality improvements, chunk size adjustments, and overlap management. This expertise is essential for a RAG Engineer aiming to create effective and efficient AI-powered information retrieval systems.

See salaries and explore the career path for RAG Engineer

Automation Engineer

An Automation Engineer designs, develops, and implements automated solutions to improve business processes. This course helps build a strong foundation in AI automation, a key component of modern automation strategies. With the skills acquired, you can create intelligent workflows using n8n, LLMs, and AI agents. You'll learn to integrate various APIs and automate tasks like email handling and data processing. The course's focus on RAG and prompt engineering will be invaluable for designing effective AI-powered automations. Learning about debugging and optimization further prepares an Automation Engineer to create robust and efficient systems. The ability to integrate AI agents into platforms like WhatsApp and Telegram is a major focus.

See salaries and explore the career path for Automation Engineer

AI Application Developer

An AI Application Developer builds and deploys applications that leverage artificial intelligence. This course helps build the skills necessary to develop AI-powered applications using n8n, LLMs, and APIs. You'll gain hands-on experience in integrating open-source LLMs, creating AI agents for various platforms, and automating tasks. The course's focus on prompt engineering and debugging strategies is crucial for developing robust and reliable AI applications. You'll also learn about security, privacy, and ethical considerations, which are essential for responsible AI application development.

See salaries and explore the career path for AI Application Developer

Integration Specialist

An Integration Specialist connects different software systems and applications to work together seamlessly. This course helps build a foundation in API integration, a core skill for an Integration Specialist. You'll learn how to integrate various APIs using n8n, including OpenAI API, Deepseek API, and Groq API. The course also covers techniques for debugging and optimizing API integrations, ensuring smooth and reliable data flow between systems. The practical examples and downloadable JSON workflows provided are invaluable for mastering the art of system integration.

See salaries and explore the career path for Integration Specialist

Solutions Engineer

A Solutions Engineer works with clients to understand their needs and design custom technology solutions. This course helps build the skills required to design AI-driven solutions using n8n, LLMs, and AI agents. You'll learn how to integrate various APIs, automate business processes, and create market-ready RAG bots. The course's coverage of prompt engineering and debugging strategies enables you to create robust and reliable solutions. Additionally, understanding security, privacy, and ethical considerations is crucial for a Solutions Engineer building AI-based systems.

See salaries and explore the career path for Solutions Engineer

AI Product Manager

An AI Product Manager oversees the development and launch of AI-powered products. This course helps build foundational knowledge of AI automation, including AI agents, LLMs, and RAG. You'll learn how to create market-ready RAG bots, integrate AI agents into various platforms, and automate tasks. The course's coverage of prompt engineering and data preprocessing provides valuable insights for product development. Also, the understanding of the security, privacy, and ethical considerations of AI ensures that you can manage AI products responsibly.

See salaries and explore the career path for AI Product Manager

AI Solutions Architect

An AI Solutions Architect is responsible for designing and implementing AI-driven solutions for businesses. This course may be useful because it provides a comprehensive overview of AI automation, including AI agents, LLMs, and RAG. You'll gain practical experience in building and integrating these technologies using n8n. The course covers essential topics such as prompt engineering, API integration, and data preprocessing. Understanding security, privacy, and ethical considerations is crucial for any AI Solutions Architect. This course also offers guidance on creating market-ready RAG bots and integrating them into websites, which are highly valuable skills for this role.

See salaries and explore the career path for AI Solutions Architect

AI Consultant

An AI Consultant advises businesses on how to leverage artificial intelligence to achieve their goals. This course may be useful through gaining a solid understanding of AI automation, including AI agents, LLMs, and RAG. You'll learn how to build market-ready RAG bots, integrate AI agents into various platforms, and automate tasks. The course also covers security, privacy, and ethical considerations, which are crucial for responsible AI consulting. Additionally, the course offers insights into marketing and selling AI solutions, providing practical guidance for an AI Consultant.

See salaries and explore the career path for AI Consultant

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This course may be useful by providing understanding of AI automation concepts such as LLMs and RAG. You'll learn how to integrate open-source LLMs, build AI agents, and automate tasks. The course also covers prompt engineering and data preprocessing techniques, which are valuable for optimizing machine learning models. Additionally, the course discusses the security, privacy, and ethical considerations of AI, which are important for responsible machine learning deployment.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes data to extract insights and build predictive models. This course may be useful through its coverage of AI automation and its components like LLMs and RAG. You'll learn how to automate data processing tasks, integrate open-source LLMs, and perform sentiment analysis. Additionally, the course covers data preprocessing techniques using LlamaIndex and LlamaParse. The optimization of RAG chatbots and the handling of data quality are also discussed. The ability to understand and implement these AI-powered automation techniques can enhance a Data Scientist's ability to extract meaningful insights from data.

See salaries and explore the career path for Data Scientist

Technical Support Engineer

A Technical Support Engineer provides technical assistance and support to customers. This course may be useful because it provides an understanding of AI automation technologies, including AI agents and LLMs. This course might help you troubleshoot issues related to AI-powered systems. Learning about debugging strategies and error handling in n8n workflows can enable you to diagnose and resolve technical problems more effectively. Additionally, the course's coverage of API integration can assist you in supporting systems that rely on API connectivity.

See salaries and explore the career path for Technical Support Engineer

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI Automation: Build LLM Apps & AI-Agents with n8n & APIs.

AI Automation

Build LLM Apps & AI-Agents with n8n & APIs

What's inside

Learning objectives

Syllabus

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Reviews summary

Practical ai automation with n8n & llms

Activities

Career center

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