Mastering Context Engineering Professional
About This Course
Generative AI is rapidly changing how professionals conduct research, create content, analyze information, and support decision-making. However, many users struggle to achieve reliable business outcomes because they lack structured approaches to providing context, evaluating outputs, and managing information effectively.
This course introduces practical Prompt Engineering, Knowledge Management, Generative AI Model Evaluation, Responsible AI and Generative AI Practices, and Workflow Digitalisation techniques that enable professionals to build reliable AI assistants without programming knowledge.
Using a real-world interview preparation project, learners will create a structured context package consisting of instructions, reference documents, examples, and evaluation criteria. They will apply Data Collection and Analysis methods to consolidate resumes, job advertisements, and supporting materials into a reusable AI knowledge base.
Participants will then build, test, improve, and evaluate an AI Interview Preparation Assistant using mainstream Generative AI platforms. Through structured exercises, learners will measure output quality, perform Generative AI Model Evaluation, identify improvement opportunities, and refine prompts and context to achieve better outcomes.
The course also emphasizes Responsible AI and Generative AI Practices, including verification of AI-generated content, protection of confidential information, management of AI limitations, and ethical AI use. Learners will conclude by identifying workplace applications and designing AI-assisted processes using Workflow Digitalisation principles.
The skills acquired are transferable across Human Resources, Learning & Development, Marketing, Sales, Operations, Customer Service, Administration, Business Analysis, and other knowledge-based functions.
What You'll Learn
Learners will apply Prompt Engineering techniques to create structured instructions that improve AI performance and consistency. They will develop Knowledge Management practices by curating, organizing, and maintaining reference materials that enable AI systems to generate grounded and reliable outputs.
Participants will learn Generative AI Model Evaluation methods to assess output quality, identify weaknesses, apply evaluation rubrics, and improve AI responses through systematic refinement. The course also introduces Responsible AI and Generative AI Practices, covering output verification, hallucination detection, ethical AI usage, confidentiality protection, and responsible deployment.
Through guided activities, learners will transform manual work processes into AI-assisted solutions using Workflow Digitalisation principles. They will conduct Data Collection and Analysis, synthesize information from resumes, job descriptions, and supporting documents, and build a fully functional Interview Preparation Assistant that can be adapted to workplace use cases.
By the end of the course, learners will possess a repeatable framework for creating AI-powered assistants that improve productivity, decision-making, content generation, research, and professional performance.
Entry Requirements
Participants are required to have the ability to converse, read and write in English. Minimum Lower Secondary Education or WPLN Level 3 and above.