Prompt Engineering for Marketers

Singapore University of Social Sciences

Prompt Engineering for Marketers (MKT563)

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: To be confirmed

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Business Administration

Schemes: To be confirmed

Funding: To be confirmed

School/Department: School of Business

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Synopsis

MKT563 Prompt Engineering for Marketers provides an in-depth exploration of prompt engineering with Generate AI tools, covering both basic and advanced techniques. Students will evaluate fundamental concepts of Generative AI and Large Language Models (LLMs), understand pitfalls such as bias, hallucination and prompt hacking, and set up learning prompt environments. Hands-on practice includes structuring data, code assistance, and automating workflows. The course advances to intermediate and advanced prompting strategies, culminating in constructing practical applications like building enhanced chatbots and developing robust AI solutions.

Level: 5
Credit Units: 2.5
Presentation Pattern: EVERY JAN

Topics

Introduction to Generative AI and LLMs
Setting Up the Learning Environment
Basic Applications Hands-On Practice
Intermediate Prompting Techniques
Advanced Prompting Techniques
Intermediate Applications Hands-On Practice

Learning Outcome

Evaluate the fundamental concepts of Generative AI, Large Language Models (LLMs), and chatbots, and identify the pitfalls such as bias, hallucination, and prompt hacking.
Design a learning environment incorporating learning prompting embeds and implement fundamental prompting techniques, such as assigning roles, showing examples, priming chatbots, and formalizing prompts.
Improve proficiency through hands-on practice with basic applications, including structuring data, assisting with code, summarizing text, and automating email flow using Zapier.
Synthesise intermediate prompting techniques, including Chain of Thought (CoT), Zero Shot Chain of Thought, and dynamic sequencing for multiple prompts.
Formulate advanced prompting strategies using techniques like self-consistency, generated knowledge, and least to most prompting.
Construct practical solutions for intermediate applications by drafting multiple-choice questions, building ChatGPT from GPT-3, and enhancing an intent-based chatbot with a knowledge base.