About This Course

Advanced AI & Prompt Engineering for IT Professionals is an intensive, hands-on training designed as a continuation of the Mastering AI in IT basic program Mastering AI in IT Programming – From Prompts to Code Optimization. This course dives deeper into the architecture of Large Language Models (LLMs), advanced prompt engineering techniques, AI workflow automation, coding optimization, security, multimodal integration, and enterprise-level deployment. Through a mix of theory, labs, and a capstone project, participants will learn how to design, optimize, and secure AI-powered IT solutions that are production-ready.

Audience Profile

This course is designed for:

• IT professionals and software developers who already use AI tools and want to deepen their expertise.
• DevOps engineers and system administrators looking to integrate AI into CI/CD pipelines and workflow automation.
• Data engineers and analysts who want to apply AI for ETL, SQL optimization, and advanced analytics.
• Cybersecurity and compliance specialists seeking to understand AI-related risks, prompt injection, and secure deployment.
• Technical project leads and solution architects who aim to design and manage enterprise-level AI-powered systems.
• Graduates of the Mastering AI in IT Programming – From Prompts to Code Optimization course, or individuals with equivalent knowledge.

At Course Completion, You Will Be Able To:

• Understand and leverage the internals of modern AI models.
• Apply advanced prompting strategies for complex problem-solving.
• Automate workflows with AI agents and integrations.
• Secure AI applications against attacks and ensure compliance.
• Optimize costs, performance, and scalability of AI deployments.
• Deliver real-world, end-to-end AI-powered solutions..

Course Outline

Module 1: Advanced AI Concepts and LLM Architecture

Theory:

• How Transformers and Attention mechanisms work.
• Fine-tuning and Reinforcement Learning with Human Feedback (RLHF).
• Prompt-tuning, LoRA, and adapters for model customization.
• LLM limitations and mitigation strategies (hallucinations, bias, context length).
• Overview of advanced models (GPT-4, Claude, LLaMA, Mistral, Mixtral).

Practical/Lab:

• Comparing outputs from different models.
• Experimenting with temperature, top-p, and other generation parameters.

Module 2: Advanced Prompt Engineering

Theory:

• Chain-of-Thought prompting and reasoning steps.
• Zero-shot, Few-shot, and Multi-shot prompting.
• Self-Consistency and ReAct prompting (Reason + Act).
• Prompt injection attacks and security best practices.

Practical/Lab:

• Designing complex prompts for multi-step problem-solving.
• Implementing ReAct prompting for IT scenarios.

Module 3: AI Automation & Workflow Orchestration

Theory:

• Integrating AI into CI/CD pipelines.
• Using AI agents (Auto-GPT, LangChain, CrewAI).
• Task orchestration with AI and APIs.

Practical/Lab:

• Building a mini-agent to automate IT tasks (e.g., code generation + testing + GitHub commit).

Module 4: Advanced Coding with AI

Theory:

• AI-assisted refactoring and performance tuning.
• Generating complex architectures (MVC, microservices).
• AI for Test-Driven Development (TDD) and unit test coverage.

Practical/Lab:

• Using AI to generate PHPUnit tests and integrate with CI.
• Optimizing PHP code with AI (profiling + improvement suggestions).

Module 5: AI for Data Engineering & Analytics

Theory:

• AI for ETL processes and data pipelines.
• Advanced SQL optimization with AI.
• AI for Big Data analysis and visualization.

Practical/Lab:

• Generating complex SQL queries and optimizing indexes.
• Using AI to create data reports and dashboards.

Module 6: AI Security & Compliance

Theory:

• Prompt injection and jailbreak attacks – detection and prevention.
• GDPR and AI – legal considerations.
• Secure AI deployment (API keys, rate limiting, logging).

Practical/Lab:

• Simulating a prompt injection attack and implementing safeguards.
• Writing secure AI scripts for sensitive data handling.

Module 7: Multi-Modal AI & Advanced Tools

Theory:

• AI for image, audio, and video processing.
• Generative models (Stable Diffusion, Midjourney) for IT projects.
• Integrating AI into applications (APIs, SDKs).

Practical/Lab:

• Using AI to generate UI components from image mockups.
• Building an app combining text + image (e.g., AI chatbot with visual responses).

Module 8: AI Performance Benchmarking & Cost Optimization

Theory:

• Measuring AI output quality (BLEU, ROUGE, perplexity).
• Cost optimization for paid AI APIs.
• On-premise vs. cloud AI solutions.

Practical/Lab:

• Benchmarking different models for the same task.
• Calculating costs and optimizing API usage.

Module 9: Advanced GitHub AI Integration

Theory:

• GitHub Copilot X and AI-driven PR review.
• AI for automated changelog and release note generation.
• GitHub Actions + AI for deployment automation.

Practical/Lab:

• Implementing an AI-assisted CI/CD pipeline.
• Auto-generating documentation and changelogs with AI.

Module 10: Capstone Project

Develop an end-to-end AI solution (e.g., AI-powered web app with GitHub integration, CI/CD, security measures, and advanced prompt logic).

Project presentations and Q&A.

Key Differences from the Basic programm Mastering AI in IT Programming – From Prompts to Code Optimization:

• Deeper theory (LLM architecture, tuning, security).
• Advanced techniques (Chain-of-Thought, ReAct, AI agents).
• More practical projects (CI/CD, AI agent, multimodal integration).
• Focus on security, optimization, and real-world production scenarios.

Prerequisites:

• Completion of the Basic Program: Mastering AI in IT Programming – From Prompts to Code Optimization, or equivalent knowledge.
• Technical background in IT or software development (e.g., system administration, DevOps, programming).
• Familiarity with:
  • Basic prompt engineering concepts (zero/few-shot prompting, temperature, top-p).
  • GitHub workflows and version control.
  • Python and/or PHP for coding exercises.
  • SQL fundamentals and data handling.
•  Comfortable with cloud tools, APIs, and working in command-line environments.
•  Recommended: Prior hands-on experience with ChatGPT, Copilot, or similar AI tools.

Not available.

Toni Njirić

Toni is a lecturer at Housing Slovenia and Croatia, where he has been covering various topics for many years and consistently achieving the highest ratings from training participants. He is also a lecturer at RIT Croatia and the founder of EZ Booker. He has over 15 years of experience in tourism and the development of large business applications. His company, EZ Booker, offers advanced solutions for managing bookings and improving communication and digital marketing for travel agencies and operators.