AI Engineer Core Track 2025: LLM Engineering, RAG, QLoRA & Agents | Course Overview & Key Highlights
Course introduction
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents, led by Ed Donner, is an intensive, project-driven program designed to turn developers into job-ready LLM Engineers in just 8 weeks. Updated in December 2025, this course focuses on real production-grade LLM systems, covering frontier and open-source models, RAG pipelines, fine-tuning with QLoRA, and autonomous multi-agent workflows.
Instead of theory-heavy lessons, you’ll build and deploy 8 full LLM applications, gaining hands-on experience with modern Generative AI stacks used in industry.
Course details
- Instructor: Ed Donner
- Last updated: December 2025
- Language: English
- Duration: ~58.5 hours (58h 31m total)
- Rating: ★4.7 / 5
- Learners: 182,061+
- Price (typical sale): ~US$7–12 (varies by Udemy promotions)
- Access: Lifetime (mobile + TV)
- Certificate: Udemy Certificate of Completion included
Key highlights
- Become an LLM Engineer in 8 weeks with a structured daily roadmap
- Build 8 real-world LLM applications (not demos)
- Master RAG, QLoRA, fine-tuning, and agentic AI workflows
- Work with frontier models (GPT, Claude, Gemini) and open-source LLMs
- Learn to run LLMs locally with Ollama
- Compare 20+ LLMs and select the best model for each use case
- Hands-on focus on performance, cost, and scalability
- Strong emphasis on production engineering, not just prompting
What you will learn
LLM Engineering Foundations
- How LLMs work under the hood
- Tokens, context windows, cost optimization
- Chat, base, and reasoning models
Models & Tooling
- Frontier models: GPT, Claude, Gemini, Grok
- Open-source LLMs: LLaMA, Mistral, DeepSeek
- Running models locally with Ollama
- OpenAI-compatible APIs and multi-provider setups
Prompting & Agentic AI
- System vs user prompts
- Tool calling & function calling
- Agent workflows and autonomous decision making
Retrieval-Augmented Generation (RAG)
- Building company knowledge agents
- Vector search and context injection
- Accuracy and hallucination reduction strategies
Fine-Tuning & QLoRA
- When to use RAG vs fine-tuning
- QLoRA and low-cost fine-tuning techniques
- Competing with frontier models using open-source LLMs
Capstone Projects (8 Real Apps)
- AI brochure generator with intelligent web scraping
- Multimodal airline customer-support agent
- Audio-to-minutes AI assistant
- Python-to-C++ performance optimization AI
- Enterprise knowledge-worker with RAG
- Price-prediction AI using frontier models
- Fine-tuned open-source competitor model
- Autonomous multi-agent deal-hunting system
Frequently asked questions (FAQ)
Q — Is this course beginner-friendly?
A — No. This course assumes Python familiarity and basic software engineering knowledge.
Q — Does it focus on real projects?
A — Yes. You build 8 production-style LLM applications, including multi-agent systems.
Q — Will I learn both frontier and open-source models?
A — Yes. The course compares and uses both closed and open-source LLMs.
Q — Are API costs required?
A — Around $5 is recommended, but you can complete the course using only open-source models.
Q — Is this useful for jobs?
A — Yes. The course is designed to make you interview-ready as an LLM / AI Engineer.
Why this course is worth it
This is not a generic “AI overview” course. It’s a career-oriented LLM engineering track that teaches how modern AI systems are actually built, optimized, and deployed. If your goal is to work with Generative AI professionally, this course gives you the skills, projects, and model intuition employers expect.
Related Certification courses by Ed Donner
-
AI Leadership Track: Gen AI, Agentic AI for Business Leaders
-
AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale
