In the rapidly evolving landscape of artificial intelligence, the way we interact with AI systems through prompts is crucial for success. The course “Applied Prompt Engineering for AI Systems” delves deeply into the art and science of designing effective prompts that drive AI performance. Rather than relying on intuition or trial and error, this course empowers you to adopt a systematic, engineering-focused approach to prompt design, testing, and optimization. You’ll learn to treat prompts as production artifacts, applying software engineering principles like versioning, A/B testing, and continuous improvement.
Throughout the course, participants will engage in hands-on labs and real-world examples that highlight the significant impact of prompt design on accuracy, cost, latency, and safety. You’ll explore prompt evaluation frameworks to measure critical metrics such as correctness, consistency, and hallucination rates. This structured approach ensures that you not only understand how to write better prompts but also how to evaluate and refine them rigorously.
In addition to optimizing prompts, the course addresses essential security strategies to safeguard against vulnerabilities such as prompt injection and bias amplification. You’ll learn how to create robust, neutral prompts that maintain predictable behavior, which is especially important in high-stakes environments. The course culminates in the introduction of Human-in-the-Loop prompting workflows, ensuring that your AI deployments are safe and responsible. By the end of this course, you will have gained practical skills to engineer, test, and scale prompts confidently in your AI projects.
What you will learn:
- Design prompts as production artifacts: versioning, control, and documentation.
- Apply A/B testing, regression tests, and dataset-driven evaluation pipelines.
- Measure key metrics: accuracy, consistency, hallucination rates, refusals, and cost per correct answer.
- Optimize prompts for accuracy, cost, and latency through controlled experimentation.
- Implement security strategies: defenses against prompt injection, jailbreaks, and biases.
- Design neutrality and constraints in prompts for predictable and equitable behavior.
- Create reproducible evaluation and monitoring pipelines for production environments.
- Set up Human-in-the-Loop workflows: review, approval, confidence scoring, and escalation.
- Debug prompts with hands-on exercises, real cases, and regression suites.
- Scale and maintain prompts with continuous improvement processes tailored to enterprise systems.
Course Content:
- Sections: 12
- Lectures: 50
- Duration: 8 hours
Requirements:
- Basic familiarity with AI or large language models (LLMs) (for example, having used tools like ChatGPT, Copilot, or similar)
- General technical literacy, such as comfort working with software tools, dashboards, or documentation,Curiosity about how AI systems behave in real-world applications and a willingness to experiment and test prompts.
Who is it for?
- AI practitioners and prompt engineers who want to evaluate, optimize, and version prompts using engineering-grade methods rather than intuition.
- Product managers and AI product owners responsible for shipping AI features that must be accurate, cost-effective, safe, and compliant.
- Software engineers and data engineers integrating LLMs into applications who need reproducible testing, regression protection, and monitoring.
- Data scientists and ML engineers looking to apply experimentation, A/B testing, and evaluation frameworks to prompt-driven systems.
- UX designers, analysts, and researchers working with AI outputs who need consistency, fairness, and predictable behavior.
- Students and early-career professionals who want practical, industry-aligned skills in modern AI system design.
- Founders and technical leaders building AI-powered products and seeking to reduce risk, cost, and unexpected failures in production.
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