Navigating ChatGPT Capabilities and User Interface
Prompt Input Tools
Users can input queries or commands efficiently using dedicated text input fields.
Conversation Management
The interface allows easy access to conversation history and menu options for better control.
Prompt Input Tools
Users can input queries or commands efficiently using dedicated text input fields.
1. Memory
ChatGPT can remember useful preferences or ongoing context to improve future responses.
Examples:
You’re always in control — memories can be viewed, edited, or deleted.
2. Custom Instructions
You tell ChatGPT how you want it to respond and what it should know about you.
Examples:
This applies across all chats.
3. Response Style & Personality
Adjusts how ChatGPT communicates — not what it knows, but how it speaks.
Examples:
Beginner Prompting with ChatGPT
Why Prompting Matters
Common Beginner Challenges
You don’t need technical skills — just a simple structure
Prompting ChatGPT is like talking to an expert or consultant.
�You need to explain your situation clearly, provide enough context, and state the outcome you want.
�Vague questions give vague answers.
Clear requests give better results.
Exploring GPT
Play Prompt
Write a Short Story
(Using ultra simple prompt)
Write a short 150 words story about an ice cream seller in orchard road. Use simple English for the layman.
Share your stories. Are they the same?
Languages, Style & Tone
Translate the story in (Mandarin, Tamil, Malay, French, etc).
Rewrite the story in (Singlish / Formal English / Use expressive shakespearian style / legal language)
Rewrite to make it a humorously or sad story
Response to Correspondence
Subject: Complaint about poor service during my stay at Good Service Hotel
Dear Manager,
I am writing to express my disappointment with the poor service I received during my recent stay at the Good Service Hotel. I stayed at your hotel from March 1st to March 5th and unfortunately, my experience was marred by the poor service provided by one of your staff, Alice. Throughout my stay, Alice was consistently unhelpful and unfriendly. On the first day, I asked her for directions to a nearby restaurant and she seemed annoyed and unwilling to provide assistance. On another occasion, I requested a late check-out and she rudely informed me that it was not possible, despite the fact that I had seen other guests being granted late check-outs. As a customer who has paid for quality service, I am extremely disappointed with the poor service provided by Alice during my stay at your hotel. I believe that a hotel of your reputation should have staff who are friendly, helpful, and attentive to the needs of guests. I would like to request that you take appropriate action to ensure that such poor service is not provided to other guests in the future. I hope to receive a response from you soon regarding the steps that you will be taking to address this matter.
Sincerely,
Adam
You are the Manager of Good Service Hotel. You received the following email from your customer. After investigation, you did not find Alice did anything wrong, however you also want to win back the customer. Write an appropriate reply to the customer.
Share your responds
Current Affairs
Prompt:
Summarize today’s top 3 news headlines in Singapore
The Power of Summarization in GPT
What is Summarization?
What ChatGPT Can Do:
Why It’s Useful:
Common Use Cases for Summarization
Work & Office
Learning & Training
Daily Life
Business
Inputs
FILES – EXCEL, CSV, DOCX, PPTX, PDF, ETC
IMAGES –
URL
YOUTUBE
Hands-on Practice: Try It Yourself
Activity Instructions:
1. Copy a long text (email, article, or report) or books or PDF, txt, .docx
2. Paste it or type URL into ChatGPT
3. Type one of the prompts below
Practice Prompts:
“Summarize this in 5 bullet points.”
“Explain this in simple language for a beginner.”
“Give me a one-paragraph summary.”
“What are the key takeaways?”
Outcome:
No idea - use history of AI sample
A Concise History of Artificial Intelligence
Introduction
Artificial Intelligence (AI) refers to the scientific and engineering pursuit of creating machines capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, perception, language understanding, and decision‑making. The history of AI is not a linear march of progress but a cyclical story of optimism, technical breakthroughs, limitations, and renewed paradigms. Understanding this history provides critical context for today’s rapid advances and ongoing debates surrounding AI’s capabilities, risks, and societal impact.
Intellectual Origins (1940s–1950s)
The conceptual foundations of AI emerged in the mid‑20th century at the intersection of mathematics, logic, neuroscience, and early computing. Alan Turing’s seminal 1950 paper, Computing Machinery and Intelligence, posed the provocative question, “Can machines think?” and introduced the Imitation Game (now known as the Turing Test) as a behavioral criterion for machine intelligence.
Early computational models such as McCulloch and Pitts’ artificial neurons (1943) and Norbert Wiener’s work on cybernetics framed intelligence as an information‑processing phenomenon. These ideas suggested that cognition might be mechanized, a radical notion at the time.
The Birth of AI as a Field (1956–1960s)
The term “Artificial Intelligence” was formally coined in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence, organized by John McCarthy, Marvin Minsky, Claude Shannon, and Nathan Rochester. The participants believed that significant aspects of intelligence could be precisely described and implemented in machines—a belief that fueled early optimism.
During this period, symbolic AI dominated. Researchers focused on rule‑based systems that manipulated symbols to emulate human reasoning. Early successes included programs for theorem proving, game playing (notably checkers and chess), and basic natural language processing. Systems such as ELIZA demonstrated that even simple pattern‑matching could produce surprisingly human‑like interactions.
Expansion and First AI Winter (1970s)
Despite early promise, symbolic systems struggled with real‑world complexity. They required extensive hand‑coded rules, were brittle outside narrow domains, and lacked common‑sense reasoning. As expectations outpaced results, funding agencies grew skeptical.
The 1970s marked the first “AI winter,” a period of reduced investment and enthusiasm. Reports such as the UK’s Lighthill Report (1973) criticized AI’s limited practical impact, leading to funding cuts and a contraction of the field.
Expert Systems and Commercial Revival (1980s)
AI regained momentum in the late 1970s and 1980s through expert systems—rule‑based programs designed to emulate the decision‑making of human specialists. Systems like MYCIN (medical diagnosis) and XCON (computer configuration) demonstrated tangible commercial value.
However, expert systems were expensive to build and maintain, highly domain‑specific, and difficult to scale. As hardware costs fell and maintenance burdens rose, interest again waned by the late 1980s, triggering a second AI winter.
The Statistical and Data‑Driven Turn (1990s–2000s)
A significant shift occurred as AI increasingly embraced statistical methods and machine learning. Rather than relying on handcrafted rules, systems began learning patterns from data. Advances in probability theory, optimization, and computational power enabled techniques such as decision trees, support vector machines, and Bayesian networks.
This era saw notable successes in speech recognition, computer vision, and information retrieval. IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997 symbolized the power of specialized, data‑intensive AI, though such systems remained narrow in scope.
Deep Learning and the Modern AI Renaissance (2010s)
The modern resurgence of AI is closely tied to deep learning—neural networks with many layers trained on large datasets using powerful GPUs. Breakthroughs in image recognition, speech processing, and natural language understanding followed landmark results such as the 2012 ImageNet competition.
Systems like AlphaGo, which defeated the world’s top Go players, and large language models capable of generating coherent text marked a qualitative leap in performance. Unlike earlier systems, these models exhibited emergent capabilities not explicitly programmed, reigniting discussions about general intelligence.
Contemporary Challenges and Reflections
Today’s AI systems are widely deployed across science, industry, and society. Yet their history cautions against uncritical optimism. Persistent challenges include interpretability, bias, robustness, energy consumption, and alignment with human values. While current models excel at pattern recognition and generation, they still lack genuine understanding, intentionality, and moral agency.
Conclusion
The history of AI is a story of evolving ideas about intelligence itself—from symbolic reasoning to statistical learning and large‑scale neural computation. Each phase contributed tools, insights, and cautionary lessons. For scholars and practitioners alike, appreciating this historical trajectory is essential to responsibly shaping AI’s future, balancing innovation with realism, and grounding technological ambition in intellectual humility.
ChatGPT’s Ability to Analyse Data & Reports
What Does “Analyse” Mean?
What ChatGPT Can Analyse:
Why This Is Powerful:
�Hands-on Practice: Analyse Sales Data with ChatGPT
Activity Instructions:
1. Upload a sales file (Excel, CSV, or table)
2. Ask ChatGPT to analyse the data
Practice Prompts:
Learning Outcome:
Date | Product Name | Category | Quantity Sold | Unit Price ($) | Total Sales ($) |
01-Jan-25 | Coffee Mug | Home | 25 | 8 | 200 |
01-Jan-25 | Water Bottle | Home | 18 | 12 | 216 |
02-Jan-25 | T-Shirt | Apparel | 30 | 15 | 450 |
02-Jan-25 | Notebook | Stationery | 40 | 5 | 200 |
03-Jan-25 | Coffee Mug | Home | 20 | 8 | 160 |
03-Jan-25 | Backpack | Accessories | 10 | 45 | 450 |
04-Jan-25 | T-Shirt | Apparel | 22 | 15 | 330 |
04-Jan-25 | Water Bottle | Home | 15 | 12 | 180 |
05-Jan-25 | Notebook | Stationery | 50 | 5 | 250 |
05-Jan-25 | Backpack | Accessories | 8 | 45 | 360 |
Sample Sales Data
Hands-on Practice: Analyze
Perform a comprehensive analysis of (company Name)’s FY20XX Annual Report. In your report include
The report to be written as a email report to an analysis team and placing key figures into a table format for easy reading.
Source:
Other Hands-on Practice
Generate Ideas or Brainstorm
a. List 10 free weekend activities for a family in Singapore
b. This is what’s in my fridge – what recipe do you have?
Trip Planning – Create Itinerary
Help me plan a 3 day trip to Malacca with food and shopping included.
Teach Me / Explain
Explain to me what is inflation in simple terms. Give me examples.
Role Play
Pretend to be a shopkeeper. Role play a customer asking for discount.
Cool Stuff
Put the data in a table format. Analyse the data and provide a summary for management.
Create visual charts and dashboard slide
compile the receipts into a summarize table format
Dear All,
Please find below the meeting minutes from our discussion on planning the New Year Party.
Meeting Details
Attendance
Agenda
Discussion & Agreements
Action Items
Please let everyone know if there are any corrections or additions. Looking forward to making this a fun and memorable celebration.
Best regards,�Andrew
Summarize this Episode