Name: Applied Prompt Engineering for AI Systems
Availability: InStock

What You'll Learn

Design robust, production-ready prompts by applying structured prompt engineering principles, including constraint design, grounding strategies.
Evaluate and optimize prompt performance scientifically using accuracy, consistency, latency, and cost metrics, rather than relying on intuition or trial.
Run A/B tests and regression tests for prompts to compare prompt variants, identify performance improvements, and prevent silent regressions over time
Debug common prompt failure patterns such as hallucinations, instruction drift, prompt injection, and misalignment, using systematic refinement workflows
Implement safety, fairness, and misuse-prevention strategies by designing prompts that reduce bias amplification, resist jailbreak attempts.
What are the requirements or prerequisites for taking your course? List the required skills, experience.

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

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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39,181 Students

16 Courses

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