About the LLM Application Development Course
After just 6 sessions, you’ll build your first AI application from scratch! And that’s only the beginning.
The course “LLM Engineering: Building AI Applications with LangChain and OpenAI” is a hands-on intensive for Python developers who want to:
- write prompts that actually work,
- build chatbots and search systems,
- create RAG architectures using their own data,
- develop multi-agent AI solutions.
Why choose the course “LLM Engineering: Building AI Applications with LangChain and OpenAI”?
Companies are rapidly adopting generative AI. Developers who can build LLM-powered applications are already getting job offers and increasing their rates.
This course will give you:
- Mini-projects with real code — practical experience you can immediately add to your GitHub portfolio.
- Cutting-edge tools — LangChain, LangGraph, OpenAI API, Streamlit.
- Instructor support — feedback on your tasks and guidance on building better AI solutions.
- A clear program — from basic prompting to advanced agent-based systems.
- Understanding, not copying — you’ll learn to build solutions from scratch instead of just replicating existing code.
Who is this course for?
This intensive is designed for you if you are:
- A Python developer
Level up from traditional coding to building AI applications that solve real business and user problems. - An engineer transitioning into AI
Gain a practical foundation to start in machine learning — without unnecessary theory, using tools applied by top companies today. - A tech lead or startup team member
Quickly prototype AI products, test hypotheses, and build MVPs powered by LLMs. - A developer looking to automate processes
Learn to build chatbots, agents, and search systems that save time and money.
Course Program
3 lectures + 3 workshops, each 1.5 hours long
Fundamentals of AI, transformers, prompt engineering, working with the OpenAI API, introduction to LangChain and Streamlit.
Creating a chatbot in Streamlit without additional data.
How RAG works: embeddings, search, vector databases.
Building a RAG app with LangChain using your own or a pre-prepared dataset.
What agents are, the ReAct approach, function calling, and implementation patterns in LangGraph.
Building an agent that interacts with services: email, calendar, integrations.
After the intensive, you will:
- Build your own AI application using OpenAI and LangChain — with working code in your GitHub repository.
- Master key tools: LangChain, LangGraph, and Streamlit — the same ones used by top companies.
- Become confident in prompt engineering — learn how to write prompts that deliver consistent, high-quality results.
- Develop RAG solutions — implement search over your own data using vector databases and embeddings.
- Learn multi-agent systems — build AI agents that interact with each other and external services.
These are practical skills you can immediately showcase in your portfolio, add to your CV, or use to grow your own product.
FAQ
Do I need knowledge of machine learning or experience in AI to take this course?
No. It’s enough to have Python programming experience and a desire to dive into the world of large language models from a practical perspective. All concepts are explained from scratch — this is an LLM application development course focused on hands-on learning.
What tools and frameworks will I learn during the course?
During the course, you will work with modern tools: LangChain, LangGraph, OpenAI API, Streamlit, and you’ll also learn how to create embeddings, use vector databases, and implement RAG approaches. You’ll gain practical skills that are already being applied in generative AI development today. Special attention is given to model integration and building multi-agent systems — all based on real examples with clear explanations.
What is RAG and why is it important for LLM application developers?
RAG (Retrieval-Augmented Generation) is a method that allows LLMs to work not only with what they were trained on, but also with new, up-to-date data from vector storage. This is especially important for building accurate, dynamic applications such as chatbots, assistants, or search systems that need to provide relevant information in real time. You’ll learn how to create embeddings, configure storage, and implement search in your projects. RAG is a key building block for scalable AI solutions.
Will I receive a certificate after completing the course?
Yes. All participants who successfully complete the course will receive a certificate of completion. It confirms your skills in building LLM-based applications, working with LangChain, OpenAI API, and implementing agent-based systems. You can add this certificate to your CV or LinkedIn profile — it’s a strong step for further growth in AI and software development.
What makes this course better than online lectures or YouTube tutorials?
This course is more than just a set of videos. You’ll get a structured program with a logical progression, hands-on workshops, coding assignments, and mentor support. All topics are explained through practice: you won’t just learn how LLMs work — you’ll actually build full AI applications. It’s the best option if you’re looking for real results, not just information.
Payment in installments without commission and interest is available from PrivatBank
LLM Course Instructor
