RAG, agents, and evaluation — moving AI from experiment to working system
An advanced course that goes past chatbot use into RAG, prompt architecture, agents, and output evaluation. Participants design AI that runs on the company's own knowledge.
What is advanced LLM training?
This course goes beyond chatbot-level use into RAG that connects internal knowledge, agentic workflows that reason across several steps, and the evaluation systems that check the results. In 2026 Korean companies are moving from AI evangelism toward rigorous verification and ROI-driven assessment, and many are finding better ROI from RAG-based hybrids than from full fine-tuning. The course builds the design skill needed to lift AI out of the experiment stage and into a system people actually run on.
Structure, not tricks
Goes past tool use into why RAG and agents behave the way they do.
Adoption judgment
You learn to decide between fine-tuning, RAG, and prompting on ROI grounds.
Verification first
Teaches the working loop for evaluating and checking AI output rather than trusting it.
Curriculum
8 hours (1 day) — what's covered
How LLMs work, and where they break
2 hoursTokens, context, and the mechanics of hallucination; how to choose between fine-tuning, RAG, and prompting
Connecting internal knowledge with RAG
2 hoursDocument embedding, retrieval-augmented generation, and hands-on design of answers grounded in company data
Agents and automated workflows
2 hoursAgentic RAG, multi-step problem solving, and designing tool calls
Evaluation, verification, and cost
2 hoursOutput quality criteria, verification loops, and adoption decisions weighed on cost and ROI
Key concepts in the curriculum
A quick look at what this course covers.
RAG knowledge linking
Scattered documents converging into one answer
Agent reasoning
Deciding on its own across multiple steps
Verification loop
Scoring AI output and regenerating it
Learning objectives
- Understand RAG and how internal knowledge gets connected to a model
- Learn prompt architecture, tool use, and evaluation techniques
- Design agentic workflows that handle multi-step tasks
- Judge AI adoption on verified output and ROI
What you take away
- A RAG design diagram for your internal knowledge
- A prompt architecture and evaluation criteria document
- One agentic workflow scenario
- A checklist for judging AI adoption ROI
Expected outcomes
- More accurate and more trustworthy answers from knowledge-grounded AI
- Adoption decisions backed by verification and ROI evidence
- Design capability for AI that can actually be operated, not just demoed
- Less business risk from hallucinated or wrong answers
How teams put this to use
Internal knowledge chatbot
Connect policies, manuals and past documents through RAG so answers cite their source
Multi-step research
Use an agentic workflow to gather, summarize and compare material automatically
Quality verification loop
Build a pipeline that scores AI answers and regenerates the weak ones
Adoption decisions
Decide per use case whether fine-tuning, RAG, or prompting gives the better return
Advanced LLM applications — FAQ
No. The focus is the structure of RAG and agents and the judgment calls around adoption, so planners and business owners keep up. We do recommend that participants already use ChatGPT regularly at work.
The core of the course is design and judgment. Exercises run on no-code and low-code tools and on diagrams; code appears only where it helps explain a concept.
Yes. We agree on use cases and sample data in advance so the RAG design exercise matches real work. Sensitive material is handled under a masking guide.
Rather than declare one answer, we give you a decision framework built on accuracy, cost, maintenance, and the nature of your data. We also cover why RAG-based hybrids have shown the better ROI across many companies.
Complexity of the task matters more than company size. Simple lookup answers are fine on basic RAG; work that needs multi-step judgment is where an agentic approach pays off. The course includes how to tell the difference.
Advanced LLM applications — get started
Tell us your goals and headcount, and we'll design the program around them.