Generated from prompt:
Create a modern, minimalistic presentation titled 'AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage'. Slide 1 — Title: AI, GenAI & Agentic AI. Subtitle: How Human Judgment Creates Real Advantage. Include tagline: From curiosity → thinking → execution → accountability. Slide 2 — Reset the Room: Emphasize that this is not a standard AI training. No demos, no tool comparisons, no hype or fear. Focus on how thinking changes when AI enters work. Quote: 'AI doesn’t replace expertise. It amplifies it.' Slide 3 — What is AI (Foundation): Define AI as pattern learning at scale. Include strengths (speed, scale, consistency) and limitations (no judgment, no context). Slide 4 — Traditional AI vs Generative AI: Contrast traditional AI (predicts outcomes, optimised for accuracy) with generative AI (generates possibilities, optimised for plausibility). Quote: 'Traditional AI predicts answers. GenAI predicts what comes next.' Slide 5 — What’s Happening Behind the Scenes: Explain how GenAI works (tokens, next-token prediction, optimised for what sounds right, not what’s true). Slide 6 — Why Hallucinations Happen: Explain design trade-offs and common causes (missing context, underspecified questions, precision tasks). Quote: 'GenAI fills gaps with patterns, not facts.' Slide 7 — Using GenAI Intelligently: Frame AI as a thinking partner. Show simple methodology: start simple, add context iteratively, ask it to challenge itself, ask for validation signals. 'Iteration beats perfect prompts.' Slide 8 — What is Prompt Engineering: Define prompt engineering simply — structuring instructions clearly, providing role/context/constraints, guiding better outputs. 'A prompt is a conversation starter, not a command.' Slide 9 — Why Prompt Engineering is Overrated: Explain misconceptions — one-shot use, replacing thinking, clever wording. Stress modern LLMs favor conversation, context, iteration. 'Good thinking beats clever prompts.' Slide 10 — A Better Mental Model: Encourage iterative thinking — add context gradually, challenge assumptions, validate outputs. 'AI improves when humans stay engaged.' Slide 11 — Does Saying “Please” Help AI?: Clarify that AI has no emotions but language patterns matter — politeness adds clarity, emotional framing adds context. 'AI doesn’t feel respect. It responds to better communication.' Slide 12 — Human–AI Collaboration: Show division of strengths: AI (speed, synthesis, pattern extraction) vs humans (framing, judgment, ethics, accountability). 'AI generates options. Humans choose wisely.' Slide 13 — From GenAI to Agentic AI: Describe Agentic AI as shifting from thinking to execution — breaks goals into steps, orchestrates workflows, uses tools, operates under guardrails. 'GenAI thinks. Agentic AI executes.' Slide 14 — Agentic AI ≠ Loss of Human Value: Explain AI removes humans from repetitive orchestration but empowers judgment, prioritisation, leadership. 'The more AI executes, the more humans must govern.' Slide 15 — Final Close: 'What Really Creates Advantage' — The future belongs to those who combine curiosity, domain depth, judgment, and AI leverage. 'AI accelerates thinking. Humans remain responsible for decisions.'
Traces AI evolution—traditional, GenAI, Agentic—debunking myths, explaining hallucinations & prompts, and stressing human strengths (judgment, curiosity, ethics) amplify AI for real advantage. (168 ch
This title slide is titled "AI, GenAI & Agentic AI." Its subtitle states that human judgment creates real advantage, progressing from curiosity → thinking → execution → accountability.
How Human Judgment Creates Real Advantage. From curiosity → thinking → execution → accountability.
The "Reset the Room" slide sets expectations by stating this is not standard AI training without demos or tools, avoiding hype or fear for real insights. It focuses on how thinking evolves with AI at work, noting that "AI doesn’t replace expertise. It amplifies it."
Source: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage
This slide defines AI as pattern learning at scale. It lists strengths like speed, scale, and consistency, alongside limitations of lacking judgment and context.
Traditional AI predicts specific outcomes from data patterns, prioritizing accuracy and precision in tasks like classification and forecasting using structured datasets. Generative AI creates novel content like text, images, or code by predicting sequences, focusing on plausibility, fluency, and creativity over exact truths.
| Traditional AI | Generative AI |
|---|---|
| Predicts specific outcomes from patterns in data. Optimized for accuracy and precision in tasks like classification, forecasting, and rule-based decisions. Relies on structured, labeled datasets for reliable results. | Generates novel content by predicting what comes next. Optimized for plausibility, fluency, and creativity in text, images, or code. Produces diverse possibilities rather than exact truths. |
GenAI breaks text into tokens and predicts the next one based on patterns, optimizing for fluent, natural-sounding outputs. It is not trained for truth or accuracy, favoring plausibility over factual correctness.
The slide "Why Hallucinations Happen" presents a quote from Dr. Elena Vasquez, AI Ethics Researcher at MIT. She states that GenAI hallucinations arise because it's optimized for plausibility over truth, filling gaps from missing context or vague queries with statistical patterns from training data, not verified facts.
> Hallucinations occur because GenAI is optimized for plausibility, not truth. Faced with missing context, underspecified questions, or precision tasks, it fills gaps with statistical patterns from training data, not verified facts.
— Dr. Elena Vasquez, AI Ethics Researcher at MIT
Source: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage
The slide presents a four-phase workflow for using GenAI intelligently: Start Simple with basic questions, Add Context Iteratively by refining details, Challenge Itself through critiques and alternatives, and Seek Validation via reasoning or sources. Each phase includes what to do and why it works, like avoiding overload, building accuracy, uncovering flaws, and confirming reliability.
{ "headers": [ "Phase", "What to Do", "Why It Works" ], "rows": [ [ "Start Simple", "Ask a basic, clear question", "Gets a quick baseline response without overload" ], [ "Add Context Iteratively", "Refine by adding details progressively", "Builds relevance and accuracy step-by-step" ], [ "Challenge Itself", "Prompt AI to critique or explore alternatives", "Uncovers flaws, biases, and gaps" ], [ "Seek Validation", "Request reasoning, sources, or confidence", "Confirms reliability and truthfulness" ] ] }
Source: AI as thinking partner. Methodology: Start simple, add context iteratively, challenge itself, seek validation. 'Iteration beats perfect prompts.'
Prompt Engineering structures instructions clearly by providing role, context, and constraints to guide AI toward better outputs. It treats prompts as conversation starters, not strict commands.
The slide debunks myths that one-shot prompts yield perfect results, clever wording replaces critical thinking, and prompt engineering is the key skill. In reality, LLMs thrive on conversation, iteration, and context, where good thinking outperforms clever prompts.
A Better Mental Model slide outlines strategies for effective AI collaboration: add context gradually, challenge assumptions, validate outputs, and iterate continuously. It stresses that AI improves when humans stay engaged.
The slide questions whether saying "please" helps AI, emphasizing that AI lacks emotions or feelings and responds to patterns, not respect. It notes that politeness aids by adding clarity to prompts and providing essential context through framing.
Source: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage
The slide "Human–AI Collaboration" contrasts AI's strengths in speed, synthesis, pattern extraction, and efficiently processing vast data to generate options with humans' strengths in framing, judgment, ethics, and accountability. It concludes that "AI generates options. Humans choose wisely," emphasizing humans' role in defining context, evaluating outcomes, and ensuring moral alignment.
| AI | Humans |
|---|
| Strengths: speed, synthesis, pattern extraction.
Processes vast data rapidly, combines insights, uncovers trends, generates diverse options efficiently. | Strengths: framing, judgment, ethics, accountability.
Define context, evaluate wisely, ensure moral alignment, own outcomes.
'AI generates options. Humans choose wisely.' |
This section header slide, titled "AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage," introduces Section 13: "From GenAI to Agentic AI." Its subtitle concisely states: "GenAI thinks. Agentic AI executes."
13
GenAI thinks. Agentic AI executes.
Source: Slide 13
Agentic AI does not diminish human value, as it removes repetitive orchestration and empowers human judgment. It enhances prioritization and leadership while demanding stronger human governance.
The slide "What Really Creates Advantage" identifies curiosity, domain depth, judgment, and AI leverage as core drivers of success. It emphasizes that AI accelerates thinking, while humans retain responsibility for decisions.
Curiosity Domain Depth Judgment
AI accelerates thinking. Humans remain responsible for decisions.
Source: AI, GenAI & Agentic AI — How Human Judgment Creates Real Advantage
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