[100% Off] Applied Prompt Engineering For Ai Systems
A practical guide to building, testing, and scaling reliable prompts in real-world AI systems
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
Description
“This course contains the use of artificial intelligence”
Modern AI systems don’t fail because models are weak—they fail because prompts are poorly designed, untested, unsafe, or unmanaged. This course teaches you how to move beyond trial-and-error prompt writing and adopt a systematic, engineering-driven approach to prompt design, testing, safety, and optimization.
You will learn how to treat prompts as production artifacts, applying the same rigor used in software engineering: versioning, A/B testing, regression testing, safety checks, and continuous improvement. Through hands-on labs, real-world examples, and structured experiments, you’ll see how small prompt changes can dramatically impact accuracy, cost, latency, safety, and reliability.
This course goes deep into prompt evaluation frameworks, showing you how to measure correctness, consistency, hallucination rates, refusal behavior, and cost per correct answer—the metrics that actually matter in production systems. You’ll build dataset-driven evaluation pipelines, design prompt variants, and run controlled A/B tests instead of relying on intuition or “what sounds good.”
You’ll also learn how to design robust and secure prompts that resist prompt injection, jailbreaks, bias amplification, and misuse. Dedicated sections focus on defensive prompt strategies, input sanitization concepts, neutrality and constraint design, and Responsible AI principles used in real enterprise systems.
Finally, the course introduces Human-in-the-Loop prompting, where you’ll design workflows for review, approval, confidence scoring, and escalation, ensuring safe deployment in high-risk or regulated environments.
Throughout the course, you will work with hands-on tests, prompt debugging exercises, real failure cases, regression suites, and continuous experimentation loops—giving you practical skills you can apply immediately in your own AI products.
By the end of this course, you won’t just write better prompts—you’ll know how to engineer, test, secure, and scale them with confidence.
