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This course offers a comprehensive, hands-on introduction to prompt and context engineering for AI, with a strong practical focus on real-world application across leading AI tools. Designed for professionals and students alike, the course moves learners from foundational understanding to confident, evidence-based practice.
Learners begin by developing an accurate mental model of how large language models work – what they can do, what they cannot do, and why this matters for writing effective prompts. Building on this foundation, the course introduces the CAR Framework (Craft, Assess, Refine) and nine research-backed techniques drawn from natural language processing, cognitive science, and human–AI interaction research.
In the applied stage, learners build a portfolio of eight mini projects using tools such as ChatGPT, Claude, Gemini, NotebookLM, and Custom GPTs — spanning content creation, data analysis, web application development, AI image generation, workflow automation, and voice-based prompting.
By the end of the course, participants not only produce better AI outputs but understand how prompts, context, and iteration work together — giving them transferable skills that remain relevant as AI tools continue to evolve.
This course is for anyone who wants to use AI tools more effectively and develop a structured, evidence-based approach to prompting. It is a good fit if you are:
By the end of this course, you will be able to:
Want to avail group discounts? Visit our “For Organizations” page to learn more about team enrollments.
In this module, learners build the foundational knowledge needed to work effectively with AI. They explore how large language models work, why AI can produce confident but incorrect outputs, and where AI succeeds and fails in professional settings. The module introduces the CAR Framework and equips learners with the first set of Craft techniques for writing clear, structured, and goal-oriented prompts.
Learning Outcomes:
In this module, learners move into the Assess stage of the CAR Framework, developing skills to evaluate AI outputs using structured reasoning techniques. They apply these skills across practical workplace scenarios including meeting documentation, content creation, and visual storytelling, building a portfolio of hands-on mini projects along the way.
Learning Outcomes:
In this module, learners apply their assessment skills to two high-value professional domains: data analysis and web development. They use AI to analyze datasets, generate insights, and create visualizations, then practice task decomposition to build and debug simple web applications — demonstrating how AI can accelerate technical work without requiring a coding background.
Learning Outcomes:
In this module, learners complete the CAR Framework by mastering the Refine stage. They explore techniques for verifying AI outputs, gathering requirements, and prompting for diversity of perspective. The module also covers responsible AI use and Custom GPTs, culminating in a capstone project where learners demonstrate all 9 CAR techniques in a single end-to-end AI workflow.
Learning Outcomes:
Upon completion of the course, you receive a signed certificate from the institute. You can share this certificate in the certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
When you click on the Enroll Now button, you will be asked to register online. Once you have completed your online registration, you will proceed to the payment section where you can choose from three options:
This course is designed for learners who want to build practical, job-ready skills in AI prompt engineering and learn how to use AI tools effectively in professional settings. It is ideal for working professionals, managers, consultants, and entrepreneurs who regularly work with documents, data, presentations, or complex workflows and want to get more reliable, high-quality results from AI.
Whether you are new to AI tools or already using them but finding the results hit-or-miss, this course provides a structured pathway — from understanding how AI actually works to hands-on practice with tools like ChatGPT, Claude, and Gemini. It is also well suited for anyone looking to build a reusable portfolio of AI-powered workflows or integrate AI more confidently and responsibly into their day-to-day work.
No formal prerequisites are required.
Yes, this course is entirely online. The live sessions will also happen virtually.
A cohort course combines elements of both asynchronous learning (through pre-recorded videos and materials that you can access at your own pace) and synchronous learning (live classes and interactive sessions scheduled at specific times).
The live classes provide an opportunity for real-time interaction with the instructor and peers, discussion of course material, clarification of doubts, and engaging in interactive learning activities.
Participants will find all necessary reading materials and assignments on the LUMSx Learning Management System (LMS). Access to the LMS will be granted upon completion of the enrollment process for the workshop. It is important to note that course materials will be made accessible in the week leading up to the commencement of the workshop, ensuring participants have ample time to familiarize themselves with the content before the official start.
While discussion and collaboration with peers is encouraged to foster a learning community, sharing or copying code/solutions is strictly prohibited. Any collaboration should be limited to discussing concepts and should not involve sharing actual code or solutions.
You have to score at least 70% to pass this course.
Yes, you will get a certificate at the end of this course.
No, you must watch all the videos and attempt all the assessments to get a course certificate.
Please forward any queries to our team on the chat widget or email your query to us at support@ilmx.org. We will only be responding to technical support queries. Content related queries cannot be entertained at the moment.