Overview
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.
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
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
“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.
Data Science Academy
Data Science Academy is a leading provider of practical, industry-focused training in data analytics, data science, and business intelligence. With a mission to make data skills accessible to everyone, the academy designs courses that bridge the gap between theory and real-world application.
Our team of expert instructors brings years of professional experience from diverse industries, including technology, finance, healthcare, and aviation. We specialize in creating hands-on learning experiences that cover the full data lifecycle—from data collection and cleaning to analysis, visualization, and storytelling.
At Data Science Academy, we believe in learning by doing. Our courses feature step-by-step projects, interactive exercises, and real-world datasets, enabling learners to build job-ready skills in Excel, SQL, Python, Power BI, Tableau, and other leading tools.
We are passionate about empowering students to turn raw data into actionable insights that drive decision-making. Whether you are a beginner exploring data for the first time or a professional looking to upgrade your skill set, our courses are designed to help you succeed in today’s data-driven world.
Recent training programs from Data Science Academy include Python for Data Analysis, SQL for Business Intelligence, and Data Visualization with Power BI—all crafted to prepare learners for real-world analytics roles.