Mohammed Al-Mahfadi

Working and Studying Smart with AI

Mohammed Al-Mahfadi

Business Transformation Lead at Accenture for Google

Leading projects in continuous improvement and automation, from process improvement to AI-powered solutions

Academic Background:

  • DBA (Pursuing) INTI
  • Master of Systems Engineering Arizona State University
  • Bachelor of Chemical Engineering University Malaysia Pahang

AI History and Future Outlook

1950s: The Foundation

Turing test establishes the theoretical foundation of artificial intelligence

In 1950, Alan Turing proposed the "Imitation Game" (now known as the Turing Test) to determine if a machine could exhibit intelligent behavior indistinguishable from a human. This concept laid the groundwork for all future AI development.

Example: Early programs like ELIZA (1966) simulated conversation by pattern matching and substitution methodology, mimicking a psychotherapist with simple "if-then" response rules.

Human Thinking

Can Machines Think?

Early AI Concepts

1980s: Expert Systems

Expert systems gain popularity in business applications and specialized domains

Expert systems captured human expertise as a set of rules to solve specific domain problems. These systems could diagnose diseases, recommend treatments, or assist with complex decisions.

Example: MYCIN was a medical diagnostic system that could identify bacteria causing infections and recommend antibiotics, often performing as well as human specialists despite using only about 450 rules.

Knowledge Base

If-Then Rules

Decision Output

2010s: Deep Learning

Deep learning breakthroughs transform AI capabilities and applications

Deep learning uses neural networks with many layers to learn from vast amounts of data. The breakthrough came with improved algorithms and increased computational power, enabling AI to recognize patterns and make decisions.

Example: In 2012, AlexNet won the ImageNet competition with 85% accuracy in image recognition, dramatically outperforming previous approaches. This sparked the deep learning revolution that transformed computer vision, speech recognition, and natural language processing.

Big Data

Neural Networks

Pattern Recognition

2022+: Generative AI

Generative AI boom revolutionizes content creation and problem-solving

Generative AI can create new content—text, images, music, and code—based on vast training data. These models understand context, generate human-like outputs, and can be instructed through natural language prompts.

Example: GPT-4 can write essays, summarize complex texts, create poetry, and even generate functional code from descriptions. DALL-E 2 can create realistic images from text descriptions, transforming "a teddy bear on a skateboard in Times Square" into a photorealistic image.

Text Prompt

Large Language Models

Generated Content

2025+: AI Agents

AI Agents & integrated tools become mainstream in daily workflows

Personalized AI assistants for students and professionals

AI agents are autonomous systems that can understand goals, make decisions, and take actions in the real world. They combine large language models with tools, memory, and the ability to plan multi-step sequences.

Example: An AI agent for a student could research topics, draft outlines, provide feedback on writing, manage deadlines, and even simulate discussions with virtual experts—all while adapting to the student's learning style and academic goals.

User Goals

Autonomous Agents

Tool Use & Actions

Effective vs Ineffective Prompts

Ineffective Prompt

Write me an article about renewable energy

Renewable energy is energy derived from natural resources that are replenished at a higher rate than they are consumed. Common types include solar, wind, hydro, geothermal, and biomass energy. These sources are important for sustainable development and reducing carbon emissions...

Why it's ineffective:

  • Too vague and general
  • No specific context or purpose
  • Lacks parameters for output format
  • Results in generic, unfocused content

Effective Prompt

Write a 300-word summary about solar energy applications in desert environments, focusing on recent innovations in the Gulf region. Include statistics on efficiency improvements and cost reductions since 2020.

Solar Energy Applications in Desert Environments: Gulf Region Innovations The Gulf region has emerged as a global leader in solar energy innovation, leveraging its abundant sunlight and vast desert landscapes. Since 2020, remarkable advancements have transformed how solar technology performs in these challenging environments. The UAE's Mohammed bin Rashid Al Maktoum Solar Park, the world's largest single-site solar installation, has pioneered dust-resistant photovoltaic panels that maintain 95% efficiency despite frequent sandstorms—a 23% improvement over 2020 models. These panels incorporate hydrophobic nano-coatings that repel sand particles and reduce cleaning requirements by 60%. Saudi Arabia's NEOM project has implemented bifacial solar panels that capture reflected light from the desert surface, increasing energy yield by 27% compared to traditional panels. This technology has reduced the levelized cost of electricity (LCOE) from $0.104/kWh in 2020 to $0.067/kWh in 2024. Temperature management innovations have addressed the efficiency challenges of extreme heat. Hybrid cooling systems developed in Qatar combine passive radiative cooling with minimal water usage, maintaining optimal operating temperatures while reducing water consumption by 80% compared to conventional cooling methods. Cost reductions have been equally impressive. Manufacturing efficiencies and regional supply chains have decreased installation costs by 42% since 2020. Government incentives across Gulf Cooperation Council countries have further accelerated adoption, with solar capacity growing at 34% annually. These innovations have positioned the Gulf region as not just a consumer but an exporter of solar technology expertise, with regional companies now consulting on desert solar implementations across North Africa and Australia. With continued investment in research and development, the region is on track to achieve its ambitious renewable energy targets while creating a sustainable model for desert regions worldwide.

Why it's effective:

  • Specific topic and regional focus
  • Clear parameters (word count, time period)
  • Requests specific data points
  • Results in targeted, valuable information

Build Your Own Prompt

Topic: [subject]
Format: [type] with [length]
For audience: [specific group]
Context: [background information]
Include: [specific data/elements]
Style: [tone/approach]

Drag components here to build your prompt

Effective LLM Usage Techniques

Proper Prompting Techniques

Specific Context

Define audience, purpose, and scope clearly

Clear Instructions

Use precise language and specific requirements

Examples Within Prompts

Demonstrate desired output format

Advanced Prompting Methods

Few-shot Learning

Providing examples before the main request

Daily Example: "Here are three recipes I like: 1) Pasta with olive oil and garlic 2) Grilled chicken with lemon 3) Banana smoothie with honey. Create a similar simple recipe using the ingredients in my fridge: eggs, spinach, cheese, and bell peppers."

Chain-of-thought

Asking AI to explain reasoning step-by-step

Daily Example: "I need to organize a birthday party for 12 people with a $200 budget. Think through step-by-step how I should allocate this budget considering food, decorations, and entertainment."

Role-playing

Assigning specific expertise to the AI

Daily Example: "Act as an experienced plumber and help me troubleshoot why my kitchen sink is draining slowly. Guide me through some DIY solutions before calling a professional."

Interactive Demonstration

Academic Research Assistant

I want you to act as a research assistant helping a graduate student at King Saud University working on a paper about water conservation technologies in agriculture. Provide a literature review of the most significant research from the past 5 years, focusing on innovations applicable to arid regions. For each study, include the authors, publication year, key findings, and limitations.

Business Report Generator

Act as a market analyst preparing a report for a retail business expanding into the UAE. Create a 500-word executive summary analyzing the e-commerce landscape in the region, including consumer behavior trends, major competitors, and regulatory considerations. Use a formal business tone and include 3-5 actionable recommendations.

Technical Documentation

You are a technical writer creating documentation for a bilingual (Arabic-English) mobile application. Write installation and setup instructions for both iOS and Android platforms, considering right-to-left language support requirements. Format as a step-by-step guide with code examples where relevant. Highlight potential localization issues that developers should be aware of.

Best Techniques for Prompting

Interactive Example

Step 1: Initial Prompt

Summarize this academic article on sustainable urban planning in Gulf cities.

I'd be happy to help summarize an academic article on sustainable urban planning in Gulf cities. However, I don't see the article attached or linked. Could you please provide the article text or a link to the article you'd like me to summarize?

Step 2: Refining the Prompt

I need you to summarize an academic article titled "Sustainable Urban Planning Strategies for Gulf Cities in the Climate Change Era" by Al-Mansouri et al. (2023). Focus on the key findings, methodology, and recommendations. Limit the summary to 300 words and highlight the most innovative approaches mentioned.

I'll need to see the actual content of the article to provide an accurate summary. However, I can explain how I would approach summarizing this article based on your requirements: 1. I would first read the entire article to understand its structure and main arguments 2. I would identify the key findings, paying special attention to the methodology used 3. I would extract the main recommendations, particularly focusing on innovative approaches 4. I would condense this information into a coherent 300-word summary 5. I would ensure the most innovative approaches are highlighted For a more accurate summary, please provide the article text or a link where I can access it.

Step 3: Optimal Prompt

Act as an academic researcher specializing in urban planning. I'll provide excerpts from the article "Sustainable Urban Planning Strategies for Gulf Cities in the Climate Change Era" by Al-Mansouri et al. (2023). The article discusses three main strategies: 1) Integration of traditional architectural cooling methods with modern technology, 2) Water-sensitive urban design adapted for arid regions, and 3) Transit-oriented development models customized for Gulf city layouts. The methodology involved case studies of 5 Gulf cities and computer modeling of temperature reductions. Key findings showed that traditional wind towers combined with smart sensors reduced cooling needs by 32%, water-sensitive designs reduced urban heat island effect by 4.7°C, and modified transit models reduced emissions while respecting cultural preferences for privacy. Summarize this information in 300 words, highlighting the innovative approaches and their potential impact on Gulf cities facing climate change challenges.

# Summary: Sustainable Urban Planning Strategies for Gulf Cities in the Climate Change Era Al-Mansouri et al. (2023) present a comprehensive framework for sustainable urban planning in Gulf cities confronting climate change challenges. Through rigorous case studies across five Gulf cities and advanced temperature modeling, the research identifies three innovative strategies with measurable impacts. ## Innovative Approaches The integration of traditional architectural cooling methods with modern technology emerges as a groundbreaking approach. The researchers demonstrate how traditional wind towers (barjeels) equipped with smart sensors and automated control systems reduced cooling energy requirements by 32%. This hybrid solution preserves cultural heritage while achieving significant energy efficiency. Water-sensitive urban design, specifically adapted for arid environments, represents another innovation. Unlike conventional Western models, these designs incorporate indigenous drought-resistant vegetation, permeable surfaces calibrated for infrequent but intense rainfall events, and greywater recycling systems. Implementation resulted in remarkable 4.7°C reductions in urban heat island effects, creating microclimates that enhance outdoor comfort without excessive water consumption. Perhaps most innovative is the customized transit-oriented development model that accommodates Gulf cultural contexts. The researchers modified traditional TOD approaches to incorporate privacy considerations, gender-separated spaces, and climate-protected walkways, resulting in increased public transportation adoption while reducing emissions and preserving social norms. ## Significance and Impact These strategies demonstrate that effective climate adaptation in Gulf cities doesn't require abandoning cultural identity for Western urban models. Instead, the research validates the effectiveness of solutions that blend traditional wisdom with technological innovation. The documented temperature reductions and energy savings provide compelling evidence for urban planners throughout the region to implement similar approaches. The study establishes a new paradigm for climate-responsive urban planning that respects the unique cultural, environmental, and social characteristics of Gulf cities.

Business brainstorming example will appear here

Code generation example will appear here

Key Takeaways

  • Be specific about your requirements and context
  • Provide relevant background information
  • Specify the desired format and length
  • Iterate and refine based on initial responses
  • Use role prompting to get specialized expertise

Student Research Assistant

Interactive Example

Step 1: Initial Research Request

I'm working on a paper about the impact of AI in education. Act as a research assistant and find the latest peer-reviewed articles from the past year on this topic. List the title, authors, journal, and link for each article.

Sure! Here are some recent peer-reviewed articles on the impact of AI in education: 1. “AI in Personalized Learning” by A. Smith et al., Journal of Educational Technology, 2024. Link: https://example.com/article1 2. “Machine Learning for Student Assessment” by B. Lee et al., Computers & Education, 2024. Link: https://example.com/article2 3. “Adaptive AI Tutoring Systems” by C. Rodriguez et al., IEEE Transactions on Learning Technologies, 2024. Link: https://example.com/article3

Step 2: Article Summary

Summarize the article “AI in Personalized Learning” by A. Smith et al. in 200 words, focusing on methodology and key findings. [Article Contetn]

In “AI in Personalized Learning” (Smith et al., 2024), the authors conducted a mixed-methods study evaluating an adaptive learning platform used by 200 students over a 12-week period. The methodology combined quantitative analysis of performance data with qualitative student interviews. Key findings include a 15% increase in learning gains compared to the control group and high student satisfaction due to real-time feedback. The study highlights that AI-driven personalization can adapt to diverse learning styles and suggests further research on long-term retention effects.

Step 3: Brainstorm Research Structure

Based on these articles, help me brainstorm an outline for my paper.

Here’s a suggested paper outline: 1. Introduction: Context and significance of AI in education 2. Literature Review: Summaries of recent studies (Personalized Learning, Student Assessment, Adaptive Tutoring) 3. Methodology Proposal: Proposed design building on existing research methods 4. Results & Discussion: Expected outcomes and analysis 5. Conclusion: Key takeaways and future research directions

Evolution of AI Agents

AI agents make decisions based on natural language, unlike traditional hardcoded workflows

Travel AI Agent Workflow

User Input

"Plan a 7-day trip to Japan for next month"

Research

Search flights, accommodations, attractions, and weather

Planning

Create itinerary with day-by-day schedule

User Confirmation

Present options and get approval

Booking

Reserve flights and accommodations

Payment

Process secure payment for all services

Finalization

Compile touristic program with recommendations

AI Agent vs Traditional Workflow

Traditional Workflow

Fixed Rules: Follows predefined, hardcoded paths and conditions

Limited Branches: Only handles scenarios developers anticipated

Structured Input: Requires specific formats and parameters

Error Handling: Breaks on unexpected inputs or edge cases

Updates: Requires developer intervention to modify behavior

VS

AI Agent

Adaptive Logic: Interprets and adapts to natural language requests

Dynamic Decisions: Determines which tools and APIs to use at runtime

Natural Input: Processes ambiguous, conversational instructions

Self-Correction: Requests clarification or adapts approach when needed

Learning: Improves performance based on feedback and interactions

Key Difference: AI agents understand the intent behind natural language requests and autonomously determine how to fulfill them, even adapting to new or unexpected situations without being explicitly programmed for each scenario.

AI Agent Decision-Making Process

Task Analysis

Break down complex requests into manageable sub-tasks

Tool Selection

Choose appropriate APIs and tools for each sub-task

Information Integration

Combine data from multiple sources into coherent output

Human Interaction

Determine when to request user feedback or confirmation

Vibe Coding and Development Democratization

Live Demo

Closing the Gap for Software Engineering Graduates

How AI is Transforming Industry Expectations

Before 2022: Entry-Level Expectations

Portfolio: Simple course projects demonstrating basic concepts

Technical Skills: Proficiency in 1-2 programming languages

Development: Focus on specific components or features

Learning Curve: Companies expected 6-12 months to reach productivity

System Design: Basic understanding of architecture diagrams

VS

After 2022: The 10X Engineer Expectation

Portfolio: Complete, production-ready applications with users

Technical Skills: Full-stack proficiency across multiple technologies

Development: End-to-end product delivery including deployment

Learning Curve: Expected to contribute significantly within weeks

System Design: Complex microservices and cloud architecture expertise

How AI Bridges This Gap

Knowledge Amplification

AI tools provide contextual expertise across multiple domains, giving graduates access to specialized knowledge previously gained only through years of experience

Accelerated Development

AI-assisted coding enables rapid implementation of complex features, allowing graduates to build sophisticated systems that previously required senior developers

Strategic Thinking

By handling implementation details, AI frees graduates to focus on system architecture, business logic, and user experience—skills that traditionally took years to develop

Holistic Understanding

AI provides context and connections across the entire development stack, helping graduates quickly develop the integrated perspective previously only available to seasoned professionals

Key Insight: AI doesn't just make graduates more productive—it fundamentally transforms what's possible at the entry level, enabling new professionals to deliver senior-level work quality and complexity from day one.

Future Outlook & Continuous Learning

Adapting Your Mindset for an AI-Driven World

Traditional Mindset

  • Learning a defined set of skills once
  • Knowledge mastery as an endpoint
  • Specialization in stable domains
  • Viewing technology as a separate domain
  • Change as an occasional disruption

Adaptive AI Mindset

  • Continuous learning as a lifestyle
  • Comfort with perpetual beginner status
  • Meta-skills over specific tools
  • Technology integration into all domains
  • Change as the only constant

Real-World Examples

Daily Life

  • Before: Spending hours searching for recipes and making shopping lists
  • Now: Asking ChatGPT "What can I make with chicken, spinach and rice?" and getting instant meal ideas
  • Before: Getting lost in unfamiliar areas or struggling with navigation
  • Now: Using AI-powered maps that adapt routes based on real-time traffic and your driving habits
  • Before: Sorting through hundreds of photos manually to find specific memories
  • Now: Using phone's AI to search photos by saying "Show me pictures from the beach last summer"
  • Before: Struggling to draft important emails or messages
  • Now: Using AI writing assistants to help compose professional emails in seconds

Professional Programming

  • Before: Spending hours debugging code with no clear solution
  • Now: Pasting error messages into GitHub Copilot for immediate troubleshooting suggestions
  • Before: Struggling to understand unfamiliar codebases when joining projects
  • Now: Using AI to generate explanations of code functionality and architecture
  • Before: Searching through documentation to find the right syntax for APIs
  • Now: Having AI generate code snippets with proper syntax based on natural language descriptions
  • Before: Learning new programming languages through trial and error
  • Now: Converting code between languages using AI and learning through the translations

Strategies for Continuous Learning

Optimizing Your Social Media Algorithm

Train your social feeds to show useful content:

  • Take 10 minutes weekly to follow 5 new accounts in your field
  • Use the "Save" feature on educational posts to signal interest
  • Actively click "not interested" on entertainment distractions
  • Create a separate "learning" profile on platforms like LinkedIn
  • Search for specific hashtags weekly (e.g., #Python, #AI, #DataScience)

Continuous Experimentation

Simple ways to keep learning new technologies:

  • Try one new AI tool each weekend for 30 minutes
  • Challenge yourself: "How would ChatGPT solve this problem?"
  • Use AI to explain concepts you don't understand yet
  • Ask AI to suggest improvements to your existing projects
  • Keep a digital journal of what you've learned from each experiment

Building Learning Networks

Easy ways to connect with knowledge sources:

  • Subscribe to one AI-focused newsletter that summarizes trends
  • Use ChatGPT to create a weekly reading list on your interests
  • Join one active Discord community related to your field
  • Ask "what should I learn next?" to AI tools monthly
  • Share what you've learned on social media to attract like-minded connections

Key Insight: Success today isn't about memorizing information, but about knowing how to ask the right questions to AI tools. The most valuable skill is building simple daily habits that keep you learning and adapting to change without feeling overwhelmed.