Master LLM Engineering & AI Agents

The AI revolution is accelerating at an unimaginable pace, and those who master Large Language Models (LLMs) and Agentic AI will define the future of technology.

The "Large Language Models (LLMs) & AI Agents Masterclass" is an intensive hands-on program designed to equip professionals and enthusiasts with the skills needed to build real-world AI applications. Whether you’re a developer, data scientist, researcher, or technology leader, this bootcamp provides the tools and knowledge to navigate and innovate in this fast-evolving space confidently.

You will begin by exploring the foundations of LLMs and agent frameworks, including how to benchmark models using LM Studio. The course then guides you through working with powerful closed-source APIs from providers like OpenAI, Gemini, and Claude. You will learn how to structure system and user messages, understand tokenization, and control outputs to build projects such as AI-powered text generators and vision-enabled calorie trackers.

As you advance, you’ll dive into the world of open-source LLMs. You will fine-tune models on Hugging Face using state-of-the-art techniques like LoRA and Parameter-Efficient Fine-Tuning (PEFT). Alongside this, you’ll gain experience designing AI-powered web applications using Gradio, creating interactive streaming apps, and building intelligent AI tutors.

A core component of the bootcamp focuses on mastering prompt engineering, including zero-shot, few-shot, and chain-of-thought prompting techniques to achieve consistent and controlled outputs. You'll also explore advanced capabilities such as building Retrieval-Augmented Generation (RAG) pipelines and working with embeddings for semantic search and knowledge retrieval.

The program concludes with the development of next-generation AI agents. You will use frameworks like AutoGen, OpenAI Agents SDK, LangGraph, n8n, and MCP to create autonomous agents capable of interacting with external systems, APIs, and other digital tools. Each module emphasizes building practical, working projects that reinforce the learning objectives and prepare you for real-world deployment.

This bootcamp is led by Dr. Ryan Ahmed, a highly experienced AI professor and educator who has taught over half a million learners globally. It is ideal for software engineers, data scientists, AI researchers, and technology professionals who want to break into the LLM and AI agent development space.

The format of the program emphasizes project-based learning with step-by-step guidance, community interaction, and access to mentorship and continuous feedback. From Day 1, you’ll be building real-world applications, positioning yourself at the forefront of this transformative field.

Enroll today, and I look forward to seeing you inside.

What's inside

Learning objectives

Understand the foundations of large language models (llms) and agentic ai, including how llms are trained, fine-tuned, and deployed.
Create and deploy intelligent autonomous ai agents using cutting-edge frameworks like autogen, openai agents sdk, langgraph, n8n, and mcp.
Explore and benchmark open-source llms such as llama, deepseek, qwen, phi, and gemma using hugging face and lm studio.
Develop real-world applications using api access to openai, gemini, and claude for text generation and vision tasks.
Apply a proven 5-step framework to select the right ai model for your business: maximizing cost-efficiency, minimizing latency, & accelerating time to market.
Evaluate llms using leaderboards like vellum and chat arena, and conduct blind tests to objectively assess ai model performance.
Design retrieval-augmented generation (rag) pipelines using langchain, openai embeddings, & chromadb for efficient document retrieval & question answering.
Build an interactive, transparent ai-powered q&a system with a gradio interface that displays answers along with source citations for enhanced user trust.
Master data validation & structured output generation using the pydantic library, including basemodel, type hints, & parsed output creation from openai models.
Build an ai-powered resume editor that analyzes gaps between a resume & job description & automatically tailors resumes/cover letters for targeted applications.
Learn how to fine-tune pre-trained open-source llms using parameter-efficient methods like lora and tools such as hugging face’s trl and sfttrainer.
Master dataset preparation and model evaluation techniques, including calculating accuracy, precision, recall, and f1-score using scikit-learn.

Apply key components in hugging face transformers library such as pipeline( ), autotokenizer( ), and automodelforcausallm( ).
Gain practical experience working with open-source datasets/models on hugging face, & apply quantization techniques like bitsandbytes to optimize performance.
Master advanced prompt engineering techniques such as zero-shot, few-shot, and chain-of-thought prompting.
Deploy multi-model ai agents using autogen, integrating llms from openai, gemini, & claude, enabling agent collaboration & human-in-the-loop oversight.
Develop and deploy agentic ai workflows using langgraph, mastering concepts like states, edges, conditional logic, and multi-stage nodes.
Design & build ai-powered booking agents using langgraph, enabling automated search & recommendation of flights & hotels through integration with external apis.
Build a data science agent team using crewai, creating specialized agents for workflow planning, data analysis, model building, and predictive analytics.
Design and automate end-to-end agentic ai workflows using n8n, integrating services like gmail, google sheets, google calendar, and openai.
Build an advanced ai tutor system using model-context-protocol (mcp) and openai agents sdk, enabling dynamic tool interoperability.
Apply classical ml models (linear regression, random forest, xgboost) within agent workflows, including dataset loading and inspection.
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Understand the foundations of large language models (llms) and agentic ai, including how llms are trained, fine-tuned, and deployed.
Create and deploy intelligent autonomous ai agents using cutting-edge frameworks like autogen, openai agents sdk, langgraph, n8n, and mcp.
Explore and benchmark open-source llms such as llama, deepseek, qwen, phi, and gemma using hugging face and lm studio.
Develop real-world applications using api access to openai, gemini, and claude for text generation and vision tasks.
Apply a proven 5-step framework to select the right ai model for your business: maximizing cost-efficiency, minimizing latency, & accelerating time to market.
Evaluate llms using leaderboards like vellum and chat arena, and conduct blind tests to objectively assess ai model performance.
Design retrieval-augmented generation (rag) pipelines using langchain, openai embeddings, & chromadb for efficient document retrieval & question answering.
Build an interactive, transparent ai-powered q&a system with a gradio interface that displays answers along with source citations for enhanced user trust.
Master data validation & structured output generation using the pydantic library, including basemodel, type hints, & parsed output creation from openai models.
Build an ai-powered resume editor that analyzes gaps between a resume & job description & automatically tailors resumes/cover letters for targeted applications.
Learn how to fine-tune pre-trained open-source llms using parameter-efficient methods like lora and tools such as hugging face’s trl and sfttrainer.
Master dataset preparation and model evaluation techniques, including calculating accuracy, precision, recall, and f1-score using scikit-learn.
Apply key components in hugging face transformers library such as pipeline( ), autotokenizer( ), and automodelforcausallm( ).
Gain practical experience working with open-source datasets/models on hugging face, & apply quantization techniques like bitsandbytes to optimize performance.
Master advanced prompt engineering techniques such as zero-shot, few-shot, and chain-of-thought prompting.
Deploy multi-model ai agents using autogen, integrating llms from openai, gemini, & claude, enabling agent collaboration & human-in-the-loop oversight.
Develop and deploy agentic ai workflows using langgraph, mastering concepts like states, edges, conditional logic, and multi-stage nodes.
Design & build ai-powered booking agents using langgraph, enabling automated search & recommendation of flights & hotels through integration with external apis.
Build a data science agent team using crewai, creating specialized agents for workflow planning, data analysis, model building, and predictive analytics.
Design and automate end-to-end agentic ai workflows using n8n, integrating services like gmail, google sheets, google calendar, and openai.
Build an advanced ai tutor system using model-context-protocol (mcp) and openai agents sdk, enabling dynamic tool interoperability.
Apply classical ml models (linear regression, random forest, xgboost) within agent workflows, including dataset loading and inspection.
Show more
Show less

Syllabus

Welcome to Part A of the Bootcamp!

Welcome to the Bootcamp!

Instructor Introduction and LLM in Action!

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Career center

Learners who complete Master LLM Engineering & AI Agents: Build 14 Projects - 2025 will develop knowledge and skills that may be useful to these careers: