JSTCyber Training Program

LLM and AI 1

Go deeper. Understand how AI actually thinks.

Move beyond surface-level AI usage. This intermediate course explores how Large Language Models work under the hood, compares major models, and teaches advanced techniques like RAG, agents, and fine-tuning concepts.

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Why go deeper into LLMs?

Most people use AI at 10% of its capability. Understanding how LLMs work unlocks the other 90%:

Getting inconsistent results because you don't understand model behavior
Can't compare models — using one tool for everything when others would work better
Missing advanced techniques like RAG that dramatically improve output quality
No understanding of tokens, context windows, temperature, or model architecture

The professionals who understand LLMs deeply will command the highest value in every industry.

Pricing

Pick a plan that works for you

Early Bird Special

$249

Limited time offer
Save $100 when you enroll today

Enroll Now →

Standard Price

$349

Full enrollment
Live instructor-led training

Enroll Now

Course Overview

Master the models that power everything

This course gives you the technical understanding to use AI strategically, not just casually.

Understand transformer architecture, tokens, and context windows
Compare GPT-4, Claude, Gemini, LLaMA, and Mistral for different use cases
Master advanced prompting: chain-of-thought, tree-of-thought, few-shot
Build RAG (Retrieval Augmented Generation) systems
Design and deploy AI agents that complete multi-step tasks
Understand fine-tuning, embeddings, and vector databases at a conceptual level

Course Curriculum

Everything you'll learn, module by module

8 weeks of intermediate AI training.

How LLMs Work: Architecture & Fundamentals

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Transformer architecture explained simply
Tokens, context windows, and attention mechanisms
How models are trained: pre-training, RLHF, and fine-tuning
Temperature, top-p, and generation parameters

Model Comparison: GPT vs Claude vs Gemini vs Open Source

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GPT-4o, Claude Sonnet/Opus, Gemini Pro — strengths and weaknesses
Open-source models: LLaMA, Mistral, Phi — when and why to use them
Benchmarking models for your specific use cases
Cost analysis and API pricing comparison

Advanced Prompting Techniques

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System prompts, persona design, and output formatting
Chain-of-thought, tree-of-thought, and self-consistency
Multi-modal prompting: text + images + documents
Prompt injection awareness and defensive prompting

RAG: Retrieval Augmented Generation

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What RAG is and why it solves hallucination problems
Embeddings and vector databases explained
Building a simple RAG system with your own documents
When to use RAG vs. fine-tuning vs. prompt engineering

AI Agents & Autonomous Workflows

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What AI agents are and how they differ from chatbots
Tool use, function calling, and multi-step reasoning
Building agents with CrewAI and LangChain concepts
Safety, guardrails, and human-in-the-loop design

Fine-Tuning, Ethics & Final Project

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When and how to fine-tune models (conceptual overview)
AI ethics: bias, safety, transparency, and governance
Final project: build and present an advanced AI system
Certificate of completion

Who This Course Is For

Is this course right for you?

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Anyone who uses AI regularly and wants to understand how it actually works

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Developers and technical professionals exploring AI integration

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AI consultants and trainers who need deeper technical knowledge

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Graduates of Intro to AI ready for the next level

Skills You'll Build

What you'll walk away knowing

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LLM Architecture

How transformers work
Tokens and context windows
Training and fine-tuning

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Model Comparison

GPT vs Claude vs Gemini
Open-source alternatives
Cost-benefit analysis

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Advanced Prompting

Chain-of-thought reasoning
Multi-modal techniques
Defensive prompting

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RAG Systems

Embeddings and vectors
Document-grounded AI
Hallucination reduction

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AI Agents

Autonomous workflows
Tool use and function calling
Multi-step reasoning

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Ethics & Safety

Bias and fairness
Governance frameworks
Responsible deployment

Meet Your Instructor

Learn from real-world experience

Tony Sanchez

Founder & Lead Instructor, JSTCyber LLC

25+ years of IT and cybersecurity experience. Master's degree in Cyber Operations. Professional AI consultant and educator serving the DC/Maryland/Virginia region.

25+ Years IT & CyberM.S. Cyber OperationsAI ConsultantK-12 → C-Suite

What You'll Build

Real projects you create

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Model Comparison Report

A comprehensive analysis comparing AI models for your specific use cases with benchmarks and recommendations.

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RAG Knowledge System

A working RAG system that answers questions from your own documents with sourced, accurate responses.

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AI Agent Prototype

A multi-step AI agent that autonomously completes a real workflow from start to finish.

FAQ

Frequently asked questions

Everything you need to know.

Do I need coding experience?

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Basic familiarity with technology is helpful but coding is not required. We explain technical concepts at a conceptual level with hands-on exercises.

How much does it cost?

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Standard price is $349. Early-bird special is $249 — a $100 savings. Group discounts available.

Will I receive a certificate?

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Yes. All students who complete the course receive a JSTCyber LLC certificate of completion.