A practical guide to building, testing, and scaling reliable prompts in real-world AI systems
Sub Category
- Data Science
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Objectives
- 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.
Pre Requisites
- 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
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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