LLM Engineering, RAG, & AI Agents Masterclass

LLM Engineering, RAG, & AI Agents Masterclass [2026] is a hands-on, systems-focused course designed to help learners build real-world applications using Large Language Models and Agentic AI. Instead of focusing only on prompts or APIs, this course goes deep into LLM engineering, retrieval systems, multi-agent workflows, and automation frameworks used in production-grade AI solutions.

The course combines open-source models, commercial APIs, and modern agent frameworks to show how intelligent systems are designed, evaluated, deployed, and automated across practical use cases.

Course Snapshot

• Instructor: Prof. Ryan Ahmed

• Learners Enrolled: 8,000+

• Content Length: ~24 hours

• Skill Level: Beginner to Intermediate

• Language: English (Auto captions), German (Auto captions)

• Certification: Certificate of completion included

• Access: Full lifetime access (mobile & TV supported)

• Rating: 4.5 / 5

What This Course Actually Covers

This masterclass focuses on how modern LLM-powered systems are engineered, not just how they are queried. Learners work across the full lifecycle of AI applications—from model selection and evaluation to RAG pipelines, agent orchestration, fine-tuning, and automation.

Rather than relying on a single platform, the course intentionally exposes learners to multiple ecosystems, helping them understand trade-offs and design choices.

AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents

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Skills & Concepts You’ll Develop

Large Language Model Foundations

• How LLMs are trained, fine-tuned, and deployed

• Understanding latency, cost, and performance trade-offs

• Applying a structured framework to select the right model for real use cases

Open-Source & Commercial LLMs

• Working with open-source models such as LLaMA, DeepSeek, Qwen, Phi, and Gemma

• Using Hugging Face, LM Studio, and Transformers APIs

• Integrating OpenAI, Gemini, and Claude for text and vision tasks

Retrieval-Augmented Generation (RAG)

• Designing RAG pipelines using LangChain, embeddings, and ChromaDB

• Building document-based Q&A systems with source citations

• Creating transparent interfaces for trust and explainability

Agentic AI & Multi-Agent Systems

• Building autonomous and collaborative AI agents

• Working with AutoGen, OpenAI Agents SDK, LangGraph, CrewAI, MCP, and n8n

• Designing agent workflows with states, edges, conditions, and human-in-the-loop control

Automation & Workflow Orchestration

• Automating end-to-end AI workflows using n8n

• Integrating external services like Gmail, Google Sheets, and calendars

• Building AI-powered booking agents and task automation systems

Fine-Tuning & Model Optimization

• Fine-tuning open-source LLMs using LoRA, TRL, and SFTTrainer

• Dataset preparation and evaluation

• Applying quantization techniques (e.g., bitsandbytes) for performance optimization

Evaluation, Validation & Structured Output

• Benchmarking models using leaderboards and blind testing

• Generating structured outputs using Pydantic

• Evaluating models with accuracy, precision, recall, and F1-score

Who This Course Is Best Suited For

• Developers building LLM-powered applications

• AI engineers working on RAG and agentic systems

• Data scientists exploring LLMs and applied AI workflows

• Professionals transitioning into AI engineering or GenAI roles

• Learners wanting exposure to multiple AI frameworks, not one tool

Common Questions Learners Ask

Do I need programming experience?
No programming experience is required, though basic Python knowledge is helpful.

Is this course about prompt engineering only?
No. Prompting is covered, but the focus is on engineering full AI systems.

Does it cover open-source models?
Yes. A significant portion focuses on Hugging Face models and tooling.

Will I build real projects?
Yes. Projects include AI tutors, resume editors, booking agents, and multi-agent systems.

Practical Value

The strength of this course lies in its breadth with structure. Learners gain hands-on experience across LLMs, RAG, agents, evaluation, and automation, helping them understand how different tools fit together in real systems.

It’s particularly valuable for learners who want to move beyond demos and understand how modern AI products are assembled and maintained.

Final Thoughts

If you’re looking to understand LLM engineering and agentic AI beyond surface-level usage, this masterclass provides a well-rounded, practical introduction. It emphasizes design decisions, evaluation, and system integration, making it a strong foundation for building real AI applications in 2026 and beyond.