About the LLM Development Course
This LLM Engineering Course is a practical, hands-on learning experience designed for developers with Python experience who want to break into the world of Large Language Models (LLMs). This LLM developer course combines theory and coding practice to give you a foundational understanding of modern AI tools like LangChain, LangGraph, and OpenAI API — and how to apply them in building working LLM-powered applications.
From prompt engineering and API integration to RAG (Retrieval-Augmented Generation) architecture and agent systems, this LLM short course dives deep into everything you need to build smart, context-aware tools powered by generative artificial intelligence. You’ll learn through code examples, mini-projects, and hands-on exercises that simulate real-world processing and development use cases.
Course Objectives for Python Developers and AI Enthusiasts
This LLM development course is designed to:
- Teach how large language models (LLMs) work, from transformers to embeddings
- Show how to build real applications with LangChain, OpenAI, and Streamlit
- Explain the fundamentals of prompt engineering for different types of tasks
- Help you understand and use RAG pipelines, vector stores, and retrieval systems
- Introduce agent-based systems and frameworks like LangGraph
- Prepare you to create, scale, and prototype LLM apps with your own data
- Provide a launchpad for those exploring generative AI, machine learning, and and LLM-based application processing
By the end of the course, learners will have a working skill set to confidently start developing with LLMs.
Who Is This LLM Course For
- Python developers with intermediate-level experience
- AI/ML enthusiasts seeking to upskill in large language modeling
- Software engineers exploring how to implement LLM use cases in projects
- Tech leads or startup teams needing to prototype AI-powered features
- Anyone looking for an LLM beginner course with real project-based experience
Course Program
Fundamentals of artificial intelligence, LLMs, transformers, prompt engineering, and using the OpenAI API. Introduction to LangChain and Streamlit.
Building a simple chatbot in LangChain without additional data using Streamlit.
Explanation of how RAG (Retrieval-Augmented Generation) works, including embeddings, retrieval, and vector knowledge bases.
Creating a RAG-based app with LangChain using your own or provided dataset.
Understanding agents, the ReAct approach, function-calling, LangGraph, and common design patterns.
Implementing a basic agent capable of performing practical actions (calendar, email, etc.).
Why Choose This LLM Engineering Course
Unlike many generic artificial intelligence tutorials, this LLM engineering course focuses on real development workflows — not just theory. You’ll get a mix of project-based learning, hands-on tasks, and exposure to tools used by professionals building cutting-edge AI apps.
You’ll walk away with:
- A clear understanding of the principles behind large language models (LLMs) and transformer architecture
- Practical skills to work with the OpenAI API and integrate various models
- The ability to write efficient prompts tailored to different use cases (prompt engineering)
- Hands-on experience building AI-powered applications using LangChain and Streamlit
- Knowledge of how to implement the RAG (Retrieval-Augmented Generation) approach with your own datasets
- The capability to create embeddings and manage vector databases effectively
- Skills to design a
FAQ
What will I learn in this LLM developer course?
You’ll learn how to use large language models in practice: working with prompts, APIs, and frameworks like LangChain and LangGraph. You’ll build applications such as chatbots, RAG systems, and basic AI agents — all with a strong grounding in modern generative artificial intelligence techniques.
Is this an LLM development course or more of a theoretical overview?
This is a hands-on LLM development course. While we cover the fundamentals, the focus is on real-world implementation with code. The course includes workshops, code examples, and step-by-step guidance to help you build your own AI applications.
Who is this LLM engineering course for?
The course is ideal for developers with Python experience who want to build intelligent apps using LLMs. It’s also great for engineers transitioning to machine learning, or product teams looking to create AI-based features.
How long is the course? Is it considered an LLM short course?
Yes, this is a short LLM course, structured around 3 core modules that you can complete in three weeks. It’s compact but intense, focused on giving you the knowledge and experience needed to start building right away.
Is this an LLM course for beginners?
Yes — it’s an LLM course for beginners, provided you have some Python experience. You don’t need a background in machine learning (ML) or natural language processing (NLP), as all concepts are clearly explained. The course builds from core fundamentals to real-world processing and application development using LangChain, OpenAI, and LangGraph.
Payment in installments without commission and interest is available from PrivatBank
LLM Course Instructor
