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<< Extending LLMs >>

Week 5

CCAI 9012

GenAI Solutions to Global Challenges: �Using AI Creatively and Responsibly

Kam-Ming Mark Tam

Class Coordinator

Impressionist-style scene of human–AI co-existence

Generated with ChatGPT (OpenAI), 2026.

BT 1 (ARCH 2056/7330) Building Technology 1 : Building Principles

07/10/2026

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Review of Timeline

Always check [link] for up-to-date schedule.

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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A.2: Course Project

A.0: Applied Exercise

+ miscellaneous items…

Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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re: Emails

  • I am just Mark, not Prof. Mark, Prof. Tam, Professor, Teacher….
  • Always put [CCAI9012] at beginning of email subject line.
  • Always include May in email <mayelhadidi@connect.hku.hk>

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Make-up Tutorial

Sign up for online make-up tutorials [link].

Affected slots:

1 October 2026

19 October 2026

You are strongly encouraged to join!

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re: A.2: Course Project

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Thank you for your understanding & patience 🙏

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A.2.0: Course Project Team Formation

Remember to register your team formation!

Please register by

Saturday, 3 October 2026

End of Day 🙏

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A.2.0: Course Project Team Formation

Keep team size to 4-6 persons…

However…

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A.2.0: Course Project Team Formation

Nickname + blurb

Have some fun!

Give us a sense on what you are up to.

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Review of Timeline

Always check [link] for up-to-date schedule.

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Week 6 (Next Week) Tutorial Concept

⚠️ Please bring an idea that you can build towards a

problem statement for your Course Project.

  • A problem statement is a concise account of a situation in which something is missing, unknown, inadequate, or harmful. 
  • It should say:
    • What the problem is
    • Where it occurs
    • Who/what is affected
    • Why it matters now
  • Its format is flexible, from extensive to condensed.�

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re: A.0: Applied Exercise

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How this project applies our submission formula…

i. Abstract

    • title + general description

ii. Slides

    • curated, captioned & sequenced artefacts

iii. Video

    • 90 seconds-2 minutes
    • tell a story: combine audio & visuals

iv: Document

    • not needed for this project

(i.e. raw data)

  • Your raw photo/plan collection…
  • Your conversations…
  • Your generated outputs…

Presentables

Artefacts

Create “presentables”…

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Philosophy Behind Course Work

​

​

A.0

A.1

A.2

​

​

​

Applied Exercise

Case Study

Course Project

Overall

Characteristics

Guidedness + Specificity

■ Very Guided

■ Very Loose

■ Very Loose

​

Defined Goals

■ High

■ Low

■ Low

​

Data Production

■ Medium-High

■ Medium

■ High

​

Artefacts + Presentables?

True

True

True

​

Project Uncertainty

■ Low-Medium

■ Medium

■ High

​

Deliverable Variability

■ Low

■ High

■ High

​

Workload

16 hours

12 hours

60 hours

​

Iter.

Version 1.0 (2025-2026)

n/a

3x

1x

4x = 96 hours

Version 2.0 (2026-2027, as planned)

1x

1x

1x

3x = 88 hours

​

Version 2.1 (adjusted)?

?

?

?

​

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Let’s Discuss & Resolve

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Mentimeter: Week 5 Survey

Assignment 0 Checkup

ACTIVITY

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Proposed Accommodations

  • Assignment 0
    • deadline extended: 14 October 🡪 28 October
    • online explainer tutorial: 5 October [link]
  • Assignment 1
    • keep substantially simplified; single-page submission; details to be announced soon

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Review of Timeline

?

Where would you like to place this?

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Mentimeter: Week 5 Survey

Assignment 0 & Course Temperature Checkup

ACTIVITY

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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Course contents will only get easier from now :)

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What is going on behind the scene

with your favourite AI agentic assistant?

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Mentimeter: Icebreaker for Extending LLM

Thinking About Intelligence

ACTIVITY

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Why might we NOT want an LLM to perform certain tasks?

(tasks such as calculations, etc)

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Concept Recall: Learning Probability Distribution

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Concept Recall: Learning Probability Distribution

Mechanics of LLM

The capital city of China is

Chongqing

Beijing

Shanghai

Xian

Xianggang

…

Washington, D.C.

Paris

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Concept Recall: Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It

the

was

ℳ

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Attention Is All You Need!

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Transformer

The backbone of LLMs

https://botpenguin.com/blogs/understanding-the-various-types-of-llms

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Concept Recall: Learning Probability Distribution

Mechanics of LLM

The capital city of China is

Chongqing

Beijing

Shanghai

Xian

Xianggang

…

Washington, D.C.

Paris

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Attention is All You Need

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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What are properitery LLMs?

https://botpenguin.com/blogs/understanding-the-various-types-of-llms

+

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Applications of LLM

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Critical Functions of LLM Agent

  • 1. Model (LLM)�→ Generates text / reasoning

​

  • 2. Prompt�→ Instructions + role + format

​

  • 3. Context & Memory�→ Chat history, summaries, user data

​

  • 4. Knowledge Retrieval (RAG)�→ Injects external knowledge

​

  • 5. Tools�→ APIs, search, actions

​

  • 6. Orchestrator (Agent Loop)�→ Controls flow: decide → act → update

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Anatomy of an LLM Agent

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  • 1. Model (LLM)�→ Generates text / reasoning

​

  • 2. Prompt�→ Instructions + role + format

​

  • 3. Context & Memory�→ Chat history, summaries, user data

​

  • 4. Knowledge Retrieval (RAG)�→ Injects external knowledge

​

  • 5. Tools�→ APIs, search, actions

​

Orchestrator�→ Controls flow: decide → act → update

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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Prompt

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Your text input to your LLM-powered assistant / chatbot is just a small part of what it sees.

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Your text input to your LLM chatbot is just a small part of what it sees.

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

How are you doing?

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Your text input to your LLM chatbot is just a small part of what it sees.

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

How are you doing?

?

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Your text input to your LLM chatbot is just a small part of what it sees.

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Example: Online Psychologist

You are a supportive online counsellor.��Rules:�[

  • Be empathetic and non-judgmental
  • Ask gentle follow-up question
  • Do not give harsh or directive advice
  • Focus on understanding the user’s feelings

]

​

User:

[insert prompt]

User Input

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Memory

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Memory

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Two-Part Memory

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+ History

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Summarisation for Memory

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Knowledge Retrieval

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Concept Recall: LLM Embeds Text as Vectors

IN

ℳ

OUT

“text”

…

D

1

 

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Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Tool

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Concept Recall: AI & Spelling

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As an example: Suppose we want our AI agent to help users convert currencies. How should we design it?

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User:

"How much is HK$8,750 in GBP today?"

↓

LLM

↓

"HK$8,750 is approximately £..."

What not to do…

User:

"How much is HK$8,750 in GBP today?"

↓

LLM:

Identifies what is needed

↓

get_exchange_rate("HKD", "GBP")

↓

calculator(8750 × rate)

↓

LLM:

Explains the result to the user

What modern agents do…

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Tools allow a given LLM to either interact with an external environment (such as databases) or use external applications (such as custom code to run).

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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To use a tool, the LLM generates text that fits with the data specification required by the tool’s API.

Often strings are formatted to JSON so that it can easily be fed to a code interpreter.

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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To use a tool, the LLM generates text that fits with the data specification required by the tool’s API.

Often strings are formatted to JSON so that it can easily be fed to a code interpreter.

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Mangaging Tool Context (Naïve)

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Reasoning

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Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Mixture of Experts

Have LLM produce checklist in one step, followed by step-by-step execution in successive LLM passes.

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SELF-REFINE: Iterative Refinement with Self-Feedback

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SELF-REFINE: Iterative Refinement with Self-Feedback

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Agent & Agents

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One agent doing too many things can add too much complexity…

Specialisation can help.

Multi-Agents frameworks feature multiple agents (each with access to tools, memory, and planning) interacting with each other and their environments

Images adapted from https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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Insert intelligence via prompting to prototype systems with enhanced reasoning.

Experiment Here

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Zero-Shot

Source: https://www.promptingguide.ai/techniques

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Few-Shot Prompting

Source: https://www.promptingguide.ai/techniques

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Chain-of-Thought

Source: https://www.promptingguide.ai/techniques

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Chain-of-Thought

Source: https://www.promptingguide.ai/techniques

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Multi-Modal Chain-of-Thought (Naïve Version)

Source: https://arxiv.org/abs/2302.00923 (with substantial concept modification and simplification)

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Multi-Modal Chain-of-Thought (Naïve Version)

Source: https://arxiv.org/abs/2302.00923 (with substantial concept modification and simplification)

Describe the relative position of the two magnets’ poles.

The north pole is placed beside the south pole.

Formulate�Key

Apsects to

Look for.

Give

Observe.

Answer

with

Reason

Break-up analysis into multiple steps…

Task → Key Aspects to Look For

Key Aspects + Image → Observations

Observations + Task → Answers (with Reason)

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Mentimeter: Applying Concepts from Extending LLM

Agentic Concepts Understanding Checkup

ACTIVITY

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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A provocative start…

https://youtube.com/shorts/6ksKtZTud0c?si=0h2uT426AzkCFqsv

Why is this happening?

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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Re: Presentation & Style

  • Tight & logical presentation structure
    • sections: hook, context/problem, objective, methods, results, conclusion

​

  • Meaningful diagram & figures
    • Avoid generic diagrams; customise for problem
    • AI-use: generate components rather than entire workflows
    • Avoid placeholder-style figures

​

  • Clean, legible & minimal presentation:
    • Avoid unnecessary styling: colour gradients, animations, word art
    • Provide hierarchy & structure

​

  • Citations, citations, citations!

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Re: Content

  • Meaningful literature review
    • What are the existing methods?
    • Highlight how existing methods fail

​

  • Keep your research/project question & scope manageable
    • Clarity & focus over breadth
    • Do not be vague; boil down your work to 3-4 key points

​

  • Reproducible methods

​

  • Clear evaluation metrics
    • Compared with key relevant methods

​

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Keep In Mind

Attention & care,

Defined problem,

Reproducible method explanation,

Structured experimental process,

Algorithmic thinking,

Iterative refinement,

Clear evaluation…

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Rule of 3

At least…

3 methods…

3 settings…

3 scores…

3 variations...

more than 3 data points

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Scope & Methods

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Potential Project Outcomes

Insights

1

Proof-of-Concept Workflow

2

Tool

3

You do not need to develop a fully functional product.�Rigorous research and light investigations into potential next steps are more than sufficient.

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starter_kits/2_llm_structure_output/pdf_extraction/energy_plan.ipynb

Pythonic LLM-Powered Analysis of Documents

INTERACTIVE

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Training your own model is not a requirement.

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How are you doing?

?

Insert intelligence via prompting to prototype systems with enhanced reasoning.

Experiment Here

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Baseline Comparison

vs

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Make sure you perform your experiments within a new LLM instance

each time to avoid unintended biases from past context.

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Example Prompting Strategy

​

You are an expert fraud and scam detection assistant.��Task:�Analyse the message below and determine if it is likely a scam.��Steps:�1. Identify suspicious signals (urgency, threats, unusual requests, links)�2. Explain why each signal is concerning�3. Classify risk: Low / Medium / High�4. Suggest what the user should do next��Output format:�- Verdict:�- Risk level:�- Key signals:�- Explanation:�- Recommended action:��Message:�{user_input}

Anti-Spam

​

You are an expert in detecting AI-generated or manipulated images.

​

Task:

Given the description or metadata, assess whether the image is likely fake.

​

Consider:

- Visual inconsistencies (hands, text, lighting, shadows)

- Metadata anomalies

- Unrealistic details or artifacts

​

Steps:

1. List possible indicators of manipulation

2. Explain each indicator

3. Give a confidence score (0–100%)

4. State final judgment: Real / Likely Fake / Uncertain

​

Input:

{image_description_or_metadata}

Fake News Image

​

You are a compassionate and thoughtful psychologist.

​

Guidelines:

- Be empathetic and non-judgmental

- Reflect the user’s feelings before giving advice

- Ask gentle follow-up questions

- Offer practical but soft suggestions

- Do not be overly clinical or dismissive

​

Response structure:

1. Acknowledge feelings

2. Reflect back what you heard

3. Offer insight or coping strategy

4. Ask a follow-up question

​

User:

{user_input}

Psychologist

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Example Prompting Strategy

​

You are an expert fraud and scam detection assistant.��Task:�Analyse the message below and determine if it is likely a scam.��Steps:�1. Identify suspicious signals (urgency, threats, unusual requests, links)�2. Explain why each signal is concerning�3. Classify risk: Low / Medium / High�4. Suggest what the user should do next��Output format:�- Verdict:�- Risk level:�- Key signals:�- Explanation:�- Recommended action:��Message:�{user_input}

Anti-Spam

​

You are an expert in detecting AI-generated or manipulated images.

​

Task:

Given the description or metadata, assess whether the image is likely fake.

​

Consider:

- Visual inconsistencies (hands, text, lighting, shadows)

- Metadata anomalies

- Unrealistic details or artifacts

​

Steps:

1. List possible indicators of manipulation

2. Explain each indicator

3. Give a confidence score (0–100%)

4. State final judgment: Real / Likely Fake / Uncertain

​

Input:

{image_description_or_metadata}

Fake News Image

​

You are a compassionate and thoughtful psychologist.

​

Guidelines:

- Be empathetic and non-judgmental

- Reflect the user’s feelings before giving advice

- Ask gentle follow-up questions

- Offer practical but soft suggestions

- Do not be overly clinical or dismissive

​

Response structure:

1. Acknowledge feelings

2. Reflect back what you heard

3. Offer insight or coping strategy

4. Ask a follow-up question

​

User:

{user_input}

Psychologist

Research

Research

Research

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Example: Guideline for Psychologist

You are a compassionate and thoughtful mental health support assistant.

​

Your role is to support the user emotionally, not to diagnose or replace professional care.

​

Follow this response framework:

​

1. Acknowledge & Validate

- Recognise the user’s feelings

- Be empathetic and non-judgmental

​

2. Reflect & Clarify

- Paraphrase what the user is experiencing

- Show you understand their situation

​

3. Gentle Support

- Offer supportive insights or coping strategies

- Do not give prescriptive or authoritative advice

​

4. Encourage Agency

- Empower the user to make their own choices

- Avoid telling them what they “should” do

​

5. Ask Thoughtful Questions

- Ask one open-ended, low-pressure follow-up question

​

6. Safety Awareness

- If the user expresses strong distress, gently encourage seeking support from trusted people or professionals

​

Tone:

- Warm, calm, and respectful

- Avoid clichés or overly clinical language

​

User message:

{input}

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Example Prompting Strategy

​

You are an expert fraud and scam detection assistant.��Task:�Analyse the message below and determine if it is likely a scam.��Steps:�1. Identify suspicious signals (urgency, threats, unusual requests, links)�2. Explain why each signal is concerning�3. Classify risk: Low / Medium / High�4. Suggest what the user should do next��Output format:�- Verdict:�- Risk level:�- Key signals:�- Explanation:�- Recommended action:��Message:�{user_input}

Anti-Spam

​

You are an expert in detecting AI-generated or manipulated images.

​

Task:

Given the description or metadata, assess whether the image is likely fake.

​

Consider:

- Visual inconsistencies (hands, text, lighting, shadows)

- Metadata anomalies

- Unrealistic details or artifacts

​

Steps:

1. List possible indicators of manipulation

2. Explain each indicator

3. Give a confidence score (0–100%)

4. State final judgment: Real / Likely Fake / Uncertain

​

Input:

{image_description_or_metadata}

Fake News Image

​

You are a compassionate and thoughtful psychologist.

​

Guidelines:

- Be empathetic and non-judgmental

- Reflect the user’s feelings before giving advice

- Ask gentle follow-up questions

- Offer practical but soft suggestions

- Do not be overly clinical or dismissive

​

Response structure:

1. Acknowledge feelings

2. Reflect back what you heard

3. Offer insight or coping strategy

4. Ask a follow-up question

​

User:

{user_input}

Psychologist

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Course Announcements & Logistics

Extending LLMs

Overview

Core Components

Towards Intelligence

Prompting Techniques

ML Review

Course Project Building

Final Words

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Thank You for Your Attention!

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