LLM Engineering, RAG, & AI Agents Masterclass [2026]
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Unclaimed ProfileThis masterclass, led by industry expert Prof. Ryan Ahmed, offers an intensive, hands-on path for developers, data scientists, and tech enthusiasts aiming to establish themselves as elite AI Engineers. Designed around modern agentic architectures, the curriculum bridges the gap between theoretical machine learning and practical, production-ready AI applications. Students will dive deep into foundational Large Language Models (LLMs) and rapidly transition into constructing advanced Retrieval-Augmented Generation (RAG) pipelines using LangChain, ChromaDB, and Pydantic. What sets this course apart is its comprehensive coverage of multi-agent orchestration frameworks like AutoGen, LangGraph, CrewAI, and the cutting-edge Model Context Protocol (MCP). Learners will master complex workflows involving tool calling, state management, and human-in-the-loop interventions. From building automated booking agents to developing collaborative data science teams, the practical projects ensure that graduates possess a robust portfolio. Whether you want to optimize model latency, fine-tune open-source models using parameter-efficient techniques like LoRA, or integrate seamless automation with n8n, this 14-day bootcamp style curriculum delivers the precise skills needed to succeed in the rapidly evolving AI landscape.
About the creator
Prof. Ryan Ahmed
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Prof. Ryan Ahmed is a distinguished educator and technology leader dedicated to making artificial intelligence and machine learning accessible to a global audience. With a profound understanding of cutting-edge tech, Ryan specializes in demystifying complex concepts such as Agentic AI engineering, LLM system architecture, and advanced retrieval-augmented generation (RAG). His teaching methodology centers on a practical, hands-on approach that bridges the gap between theoretical data science and enterprise-ready application development. Whether guiding his students through building custom Python-based AI agents from scratch or configuring enterprise workflows using Microsoft Copilot Studio, Ryan ensures every lesson is grounded in real-world utility. He…Show more
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What You'll Learn
- 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.
Best For
- Software developers looking to transition into AI and agentic systems architecture.
- Data scientists seeking to build production-grade RAG pipelines and multi-agent workflows.
- Technical professionals who want to master cutting-edge orchestration frameworks like LangGraph and CrewAI.
- Engineers aiming to deploy and fine-tune open-source LLMs using efficient techniques like LoRA.
Not For
- Complete beginners with zero coding experience or no foundational knowledge of Python.
- Professionals seeking a strictly theoretical or academic introduction to machine learning mathematics.
- Learners exclusively interested in no-code or low-code AI solutions without understanding backend LLM integration.
- Individuals looking for a quick, non-technical overview of AI ethics or policy rather than hands-on engineering.
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