Study information

Introduction to Prompt Engineering - 2024 entry

MODULE TITLEIntroduction to Prompt Engineering CREDIT VALUE15
MODULE CODECOM2017 MODULE CONVENERDr Avon Huxor (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

This course introduces you to the emerging field of prompt engineering, which involves designing and refining prompts to effectively utilize AI and language models, such as chatGPT. No prior computing knowledge is required. The course will cover fundamental concepts, practical applications, and ethical considerations, providing students with the skills to craft effective prompts for various AI tools. It requires no prior knowledge of computing or of language models.

AIMS - intentions of the module
  • Understand the basics of AI and language models, and the metaphors used to understand these.
  • Learn the principles of prompt engineering, and develop the skills to create and refine prompts for a range of applications.
  • Explore the social, legal and ethical implications of language models.
  • Gain practical experience through hands-on projects and exercises.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Develop skills to craft effective prompts for various applications
2. Explain, at a non-technical level, how language models work.

Discipline Specific Skills and Knowledge

3. Explain the ethical and social implications of the creation and use of language models.
4. Evaluate approaches to the exploration of innovative digital information tools.

Personal and Key Transferable / Employment Skills and Knowledge

5. Analyse relevant issues in the representation and use of knowledge in a chosen domain.
6. Demonstrate the key cognitive skills of critical and reflective thinking.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

The module will cover:

  • The history and nature of AI and NLP
  • The nature of large language models (LLMs)
  •  How to use prompts to obtain useful results from LLMs
  • Evaluating the results from prompts
  • The ethical and social implications of LLMs
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 22 Lectures and seminars
Scheduled Learning and Teaching 11 Practical work writing prompts
Guided Independent Study 117 Background readings, and practice.

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Report and diary 70 1500 words 1-6 Written feedback
Quiz 30 1 hour 1,2,3 Class feedback

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Report and diary Report and diary 1-6 Referral/deferral period
Quiz Quiz 1,2,3 Referral/deferral period

 

RE-ASSESSMENT NOTES

Deferral – if you have been deferred for any assessment you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of referral will be capped at 40%

 

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Web based and Electronic Resources:

  • The Prompt Report: A Systematic Survey of Prompting Techniques’  Schulhoff et al.  https://arxiv.org/html/2406.06608v1
  • ‘Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions’ , Ma et al. https://arxiv.org/html/2405.02345v1

Other Resources:

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 5 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Wednesday 11th September 2024 LAST REVISION DATE Thursday 19th September 2024
KEY WORDS SEARCH Prompt engineering, generative AI

Please note that all modules are subject to change, please get in touch if you have any questions about this module.