Prompt Engineering Techniques

Program Description

In this course, students will uncover the intricacies of crafting prompts that optimize interaction with Large Language Models (LLMs), a coveted skill in today’s tech-centric landscape. They will explore techniques for enhancing chatbot memory and navigating vector search strategies and databases. The course will serve as a comprehensive roadmap to mastering prompt engineering, empowering you to elicit meaningful responses from LLM applications and tackle prompt hacking challenges. With these proficiencies, students will adeptly craft compelling prompts for LLMs and proficiently analyze vector search outcomes, preparing for impactful roles in the AI and machine learning sector.

Learning Objectives / Outcomes

Upon successful completion of this program, the student will have reliably demonstrated the ability to:

Understand the prompts
Create effective prompts
Utilize open AI at various platforms
Work with open AI and google bard

Career Occupation

Career options available but not limited to:

AI Research Assistant
AI Research associate consultant
AI Analyst Trainee

Admission Requirements

To be eligible for admission, applicants must meet the following criteria:

Basic Admission Requirements

High School Completion: Applicants must have a high school diploma or an equivalent qualification.
Age Requirement: Minimum 19 years of age.
Mature Student Status*: Applicants who have not completed high school and are at least 19 years of age, may apply as a mature student.

*Mature student status may be granted to applicants who are over 19 years old and have not completed secondary school or equivalent. The applicants will be considered for admission based on the skills and experience they have acquired since leaving school. The applicant is required to provide the most recent transcripts or proof of academic accomplishments, a resume or summary of professional accomplishments, and two letters of recommendation from both their current and previous employers. The applicant may be interviewed by the Registrar’s or Academic office to further assess their suitability for admission to the program of study.

Methods Of Evaluation

Students demonstrate their learning in the following ways:

Evaluation Method	Weight
Assignment 1	25%
Assignment 2	25%
Final Project/Exam	50%
Total	100%

Program Duration

40 Instructional hours/ 2 WEEKS

Homework Hours

Students should be prepared to invest approximately 20 hours per week.

Delivery Methods

In-class instruction: 100% hours of instruction delivered in a classroom or other setting, where instructors share the same physical space as students.
Distance education: 100% hours of instruction, excluding work experience hours, if applicable, delivered remotely from a BC location.
Combined delivery: (both in-class and distance): Instruction provided through a combination of in-class and distance delivery. Program may include a work experience component (in-person).

[50] % of combined program will be provided by distance (online) delivery.

If distance or combined delivery is indicated, the online components are:

Synchronous, meaning students attend classes virtually in ‘real time’ with instructors and classmates.
Asynchronous, meaning students and instructors do not meet in ‘real time’. There is no live video lecture portion of the program. Students in a program or course that is delivered asynchronously may move through assignments at their own pace, supported by online resources such as recorded lectures, reading material, assignments and discussion groups.
Combination of both synchronous and asynchronous.

Program delivery is [20] % synchronous and [80] % asynchronous.

Equipment Required

Students who are studying online will require access to high-speed internet, a laptop and software that enables document preparation, spreadsheets, presentation tools, and graphics.

Program Organization

Course Title/Work Experience Component* (in order of delivery)	# of Hours of Instruction*	Delivery Method (In-class, Distance, or Combined)	Distance/Combined Delivery Description (Synchronous or Asynchronous)
Prompt Engineering Techniques	40 hours	In-class, Distance, or Combined	Combined – Distance – synchronous, Distance – asynchronous, Distance – both synchronous and asynchronous

Completion Requirements

To successfully complete the program, students must meet ALL of the following requirements:

Maintain a minimum attendance rate of 80%
Achieve a minimum overall score of 70% to successfully pass.

Required Course Materials

Suggested Textbooks

Demystifying Prompt Engineering: AI Prompts at Your Fingertips (A Step-By-Step Guide)” by Nathan Hunter
Mastering ChatGPT Prompt Engineering: A Practical Handbook for Harnessing Conversational AI” by Igor Balk

Prompt Engineering Techniques

Module	Topics
1	Introduction to Prompt Engineering What is Prompt Engineering? Importance of Prompt Engineering Applications of Prompt Engineering
2	Understanding Prompts Definition and Types of Prompts Components of a Prompt Understanding Prompt Context Problems and Challenges with Prompts
3	What is Prompt Engineering? Eliciting Desired Response Eliciting Desired Response – Hands On Clarity and Specificity in Prompts
4	Dealing with Ambiguity Handling Sensitive Topics and Content Safeguards Importance of Context in Prompt Design
5	Prompt Strategies for Better Output Useful Prompt Templates
6	Creating Effective Prompts Case Studies: Prompt Engineering Examples Step-by-Step Process of Creating Prompts Prompt engineering for Text Summarization
7	Prompt engineering for Information Extraction Prompt engineering for Question Answering Prompt engineering for Text Classification
8	Prompt engineering for Code Generation Prompt engineering for Reasoning Analyzing and Evaluating Prompt Performance
9	Working with OpenAI API Overview of OpenAI API ChatGPT PlayGround How to setup ChatGPT add-on?
10	Get started with ChatGPT in Google Docs Get started with ChatGPT in Google Sheets Data generation trick for ChatGPT in Google Sheets
11	Advanced Prompt Engineering Harnessing the Power of System Messages and Instructions Dealing with Biases in Prompt Responses Mitigating Inappropriate or Unwanted Responses
12	Engineering Prompts for Multilingual and Multicultural Contexts Building Iterative and Interactive Prompt Chains
13	Future of Prompt Engineering and AI Conversations Evolution and Trends in AI Conversational Models Innovations and Opportunities in Prompt Engineering Challenges and Ethical Considerations in Prompt Engineering
14	Working with other popular AI tools Bard Claude

Overview

40 hours / 2 Weeks

Certificate

In-Class, Distance, Combined