Applied Prompt Engineering for AI Systems

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Applied Prompt Engineering for AI Systems
Published 1/2026
Duration: 6h 23m | .MP4 1920x1080 30fps® | AAC, 44100Hz, 2ch | 3.56 GB
Genre: eLearning | Language: English

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 becauseprompts are poorly designed, untested, unsafe, or unmanaged. This course teaches you how to move beyond trial-and-error prompt writing and adopt asystematic, engineering-driven approach to prompt design, testing, safety, and optimization.

You will learn how to treat prompts asproduction artifacts, applying the same rigor used in software engineering:versioning, A/B testing, regression testing, safety checks, and continuous improvement. Throughhands-on labs, real-world examples, and structured experiments, you'll see how small prompt changes can dramatically impactaccuracy, cost, latency, safety, and reliability.

This course goes deep intoprompt evaluation frameworks, showing you how to measurecorrectness, consistency, hallucination rates, refusal behavior, and cost per correct answer-the metrics that actually matter in production systems. You'll builddataset-driven evaluation pipelines, designprompt variants, and runcontrolled A/B testsinstead of relying on intuition or "what sounds good."

You'll also learn how to designrobust and secure promptsthat resistprompt injection, jailbreaks, bias amplification, and misuse. Dedicated sections focus ondefensive prompt strategies,input sanitization concepts,neutrality and constraint design, andResponsible AI principlesused in real enterprise systems.

Finally, the course introducesHuman-in-the-Loop prompting, where you'll design workflows forreview, approval, confidence scoring, and escalation, ensuring safe deployment in high-risk or regulated environments.

Throughout the course, you will work withhands-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 toengineer, test, secure, and scale them with confidence.

Who this course is 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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