[100% Off] Agentic Ai &Amp; Prompt Engineering Bootcamp: Build Ai Employees
Master RAG, Prompt Engineering, Automation, Safety, Debugging & Production AI Systems with Hands-On Projects
What you’ll learn
- Build and deploy real AI agents that can handle tasks
- automate workflows
- and operate like AI employees
- Master prompt engineering from basics to advanced techniques including structured outputs and optimization
- Design AI systems with memory
- context
- and decision-making logic for smarter and more reliable agents
- Create RAG (Retrieval-Augmented Generation) systems to connect AI with custom data and improve accuracy
- Develop autonomous workflows and multi-agent systems that run 24/7 with scheduling and triggers
- Build real-world projects like AI Customer Support
- Business Analyst
- Email Automation
- and Task Automation agents
- Deploy AI applications using VPS
- APIs
- and production-ready setups
- Optimize systems with cost control
- caching
- prompt efficiency
- and performance improvements
- Implement AI safety techniques including guardrails
- validation
- and hallucination handling
- Learn debugging
- testing
- and monitoring to build reliable and scalable AI systems
Requirements
- No prior AI or machine learning experience required — this course is beginner-friendly
- Basic computer skills (using a browser
- installing software
- running simple commands)
- A laptop or desktop with internet connection
- Willingness to learn and build hands-on projects
Description
“This course contains the use of artificial intelligence”
Welcome to the AI Engineering Bootcamp: Build, Deploy & Scale AI Agents — a complete, hands-on course designed to take you from beginner to production-level AI engineer.
This course is not about just using AI tools. It’s about learning how to build real AI systems, create AI agents, and design AI-powered workflows that work in real-world environments. You’ll start by understanding the fundamentals of Agentic AI and what it truly means to build an AI Employee—a system that can think, act, automate, and operate continuously.
We begin with strong foundations in Prompt Engineering, where you’ll learn how LLMs think, how to design high-quality prompts, and how to use frameworks like Role → Task → Context → Output. You’ll then move into advanced prompting techniques including Chain of Thought (CoT), structured outputs (JSON), and prompt optimization, giving you professional-level control over AI behavior.
From there, you’ll set up your environment using tools like Claude Code, understand how to work with APIs, and build your first AI-powered workflow. You’ll then unlock one of the most powerful concepts in AI—memory and context, learning how to create agents that remember users and avoid hallucinations.
As you progress, you’ll build your first AI agent architecture, designing systems that process input → logic → output, and adding decision-making capabilities. Then comes the real transformation—creating autonomous workflows that run 24/7 using event-driven systems, cron jobs, and trigger-based automation.
You’ll go even deeper by building multi-agent systems, where multiple AI agents collaborate, delegate tasks, and execute workflows like real teams. Then, you’ll master one of the most in-demand enterprise skills—RAG (Retrieval-Augmented Generation)—allowing your AI to connect with custom data, improve accuracy, and reduce hallucinations.
The core of this course is your portfolio-ready projects, where you will build real-world systems including an AI Customer Support Agent, an AI Business Analyst, an AI Email Automation Agent, and a Task Automation Agent. These projects are designed to give you job-ready skills and practical experience.
Finally, you’ll learn how to take your systems to production with deployment, VPS setup, and running AI agents 24/7. You’ll also master optimization techniques like cost control, prompt efficiency, caching, performance tuning, and scaling AI systems.
To make you a complete AI engineer, the course ends with advanced topics in AI Safety, including handling hallucinations, testing AI outputs, implementing guardrails & validation, debugging workflows, and building monitoring systems for real-world reliability.
By the end of this course, you won’t just understand AI—you will be able to design, build, deploy, optimize, and scale production-ready AI systems.
