If you are a Python developer, AI engineer, or software architect who wants to build secure LLM applications and defend against emerging threats like prompt injection and jailbreaks, this course stood out because it offers a hands-on, project-based masterclass that takes you through building, attacking, and securing a real-world AI assistant, covering everything from RAG and tool calling to memory, AI agents, and a comprehensive security gateway.

This Course Offers

• A complete hands-on project building a secure AI assistant: You will build SecureOps, an enterprise AI assistant, progressively adding chat, RAG, tool calling, memory, and agent capabilities, then attacking and securing each new feature.
• Practical skills in identifying and mitigating LLM vulnerabilities: You will learn to execute and defend against prompt injection, jailbreaks, RAG poisoning, tool manipulation, and memory attacks, using real-world scenarios.
• Expertise in building a comprehensive AI security gateway: You will build a centralized security gateway with prompt validation, jailbreak risk scoring, RAG sanitization, tool authorization, memory validation, agent policy enforcement, and audit logging.
• Knowledge of production deployment and security controls: You will package the completed assistant with Docker, connect it to local models via Ollama, and implement human-in-the-loop approvals, output validation, and incident detection.

Why We Love This Course

1. It uses a "build, attack, secure" methodology. You can tell the course is designed to give you deep, practical understanding. By building a feature, attacking it, and then securing it, you learn the vulnerabilities and defenses in a memorable, hands-on way.
2. It covers the full spectrum of LLM security. The course addresses prompt injection, jailbreaks, RAG security, tool calling security, memory poisoning, and agent security, providing a comprehensive view of AI application security.
3. It is project-based with a real-world application. The SecureOps assistant is a realistic enterprise tool, making the skills you learn directly applicable to production AI systems.
4. It is designed for developers with basic Python knowledge. The course is accessible to developers who want to specialize in AI security, with clear, step-by-step instructions and practical labs.

LLM security is a critical and rapidly evolving field, and this course provides a clear, practical, and comprehensive path to building that expertise. It is backed by a money-back guarantee if it does not meet your expectations.

• This course is perfect for Python developers building AI-powered applications, AI and Generative AI engineers, and developers working with OpenAI or Ollama.
• It is ideal for engineers building RAG systems, AI agents, and tool-calling applications, as well as software architects and technical leads designing production AI systems.
• The course is also great for students and developers looking to specialize in AI application security.

• Basic Python programming knowledge, including variables, functions, classes, and modules, is required.
• A computer running Windows, macOS, or Linux with permission to install software is needed.
• No prior knowledge of AI security is required; everything is covered step by step.
• An OpenAI API key is optional, as the course also covers using free local models with Ollama.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.