Prompt Playbook: AI Fundamentals PART 1
I've noticed something interesting in my work with entrepreneurs, advisors, and consultants who are starting to use AI.
Many feel they're missing foundational knowledge about how these systems actually work. They can follow tutorials on prompting and use the tools, but there's this nagging uncertainty – like driving a car without understanding what's happening under the hood.
I tell a lot of them that honestly…it doesn’t matter that much! Just learn to use the tools.
BUT understandably people still worry. They lack confidence because of this gap in their knowledge. What if a client asks about how ChatGPT works? What is a customer asks a technical question?
This knowledge gap isn't just academic – it has real business consequences. I've seen consultants struggle to explain AI capabilities to clients, entrepreneurs waste time trying approaches that fundamentally won't work with current AI, and advisors making strategic recommendations based on misconceptions about what AI can and can't do.
This week, we're going to fix that. We'll build a practical foundation of AI knowledge that helps you make better business decisions, communicate more effectively about AI, and cut through the hype to see the real opportunities.
Summary
The Basics of AI
The journey from traditional programming to machine learning to generative AI
Why large language models represent a fundamental shift
How computers evolved from following instructions to "thinking"
The pivotal transition from symbolic AI to neural networks
What this means for your business approach to AI
From "If-Then" to "I Think..."
Last week I was chatting with a developer friend who's been coding since the early 90s. He said something that stuck with me: "In the old days, I had to tell the computer exactly what to do, step by step. Now I just ask it nicely, and it figures things out on its own."
The most profound change is in how we interact with computers. We've moved from
Programming paradigm: "I will tell you exactly what to do, step by step."
AI paradigm: "I'll tell you what I want to achieve, and you figure out how to do it."
We've gone from painstakingly programming explicit instructions to having conversations with machines that seem to "get it." This fundamental shift isn't just interesting tech trivia – it completely changes how entrepreneurs like us can leverage technology to build and grow our businesses.
Traditional programming is like creating a detailed recipe: "If this happens, then do that." Computers follow these explicit instructions perfectly but have zero flexibility. If you didn't anticipate something in your code, the computer would be completely stuck.
Machine learning introduced a new approach: instead of writing explicit rules, we started showing computers many examples and letting them detect patterns. This was a bit like training a dog – it couldn't explain why it made certain decisions, but it could recognise patterns with remarkable accuracy after seeing enough examples.
What we have now with generative AI and large language models is something entirely different. These systems have ingested vast amounts of human knowledge and can now generate new content, reason through problems, and engage in meaningful dialogue.
It's like the difference between
A calculator (traditional programming)
A trained animal that can categorise things (machine learning)
A knowledgeable assistant that can understand, create, and explain (generative AI)
A Brief History: Key Milestones
AI's evolution hasn't been a straight line – it's been a series of breakthroughs, setbacks, and paradigm shifts spanning over 70 years. It’s not new.
Basically we’ve been trying to build AI since computers were imagined: Alan Turing envisioned how computers could be build and at the same time speculated that they might one day simulate human intelligence. Pretty smart guy!
Here are the pivotal moments
1950s: The Birth of AI Alan Turing proposed his famous test for machine intelligence, and the field of AI was officially named at the Dartmouth Workshop in 1956. Early researchers were wildly optimistic, believing true machine intelligence was just around the corner.
1970s-1980s: The First AI Winter Early enthusiasm gave way to disappointment as researchers hit hard limitations. Funding dried up as promised breakthroughs failed to materialise.
1990s-2000s: The Rise of Machine Learning Rather than trying to program intelligence directly, researchers focused on statistical approaches where systems could learn from data. This change in direction slowly revitalized the field.
2012: The Deep Learning Revolution A neural network dramatically outperformed traditional methods in the ImageNet competition, triggering massive investment in deep learning approaches.
2022-Present: The Generative AI Era The public release of ChatGPT and similar systems brought sophisticated AI capabilities to the general public, democratizing access to these powerful tools virtually overnight.
For those interested in a deeper dive into AI history, I highly recommend
This excellent video overview:
Michael Wooldridge's book, "The Road to Conscious Machines"
Symbolic vs. Neural: The Great Transition
Understanding the shift from older approaches to today's AI requires grasping the fundamental divide between two competing philosophies:
Symbolic AI (also called "Good Old-Fashioned AI" or GOFAI): This approach, dominant in AI's early decades, used explicit symbols and rules to represent knowledge and reasoning.
It's like programming a computer with logical statements: "If X, then Y." These systems were transparent – you could trace their reasoning steps – but they struggled with ambiguity and required manually specifying every rule.
This approach (and its subsequent failure) led to the first AI Winter. Hype was huge but the results were limited. This led to funding being pulled and AI becoming a joke in academic circles.
Neural Networks: These systems learn patterns from data without explicit rules. Modern deep learning is the most successful neural approach, using interconnected layers of artificial "neurons" to identify patterns. Neural systems handle ambiguity well but work as "black boxes" – their internal reasoning isn't easily interpretable.
These systems fell under the “connectionist” approach. They were initially ignored by the symbolic AI practitioners and seen as a weird kooky approach that would come to nothing.
For decades, these approaches competed. Symbolic AI dominated early AI research but ultimately hit walls when trying to handle the messiness of the real world. You simply couldn't program enough rules to cover all possibilities.
Neural approaches, especially deep learning, ultimately surged ahead by letting the computer discover its own patterns from data rather than following human-programmed rules. We basically got out of the damn way.
This transition from symbolic, rules-based AI to neural, pattern-matching AI represents perhaps the most important shift in the field's history. It’s led to where we are today.
This evolution set the stage for today's large language models, which we'll explore in detail in the next Part. These systems don't follow explicitly programmed rules about language; they learn patterns from massive text datasets. That's why they can seem so remarkably flexible compared to earlier systems.
But remember that what we now call AI is just a part of the wider history, trends and approaches that have come before now!
Situating LLMs in the AI Landscape
There's often confusion about how different AI terms relate to each other, so let's clear that up right now.
Importantly this is going from widest to most narrow. Each of these fits inside the prior.
Artificial Intelligence (AI): The broadest umbrella term, covering any technique that enables computers to mimic human intelligence.
Machine Learning (ML): A subset of AI where systems learn from data rather than explicit programming. This includes many approaches like decision trees, neural networks, and support vector machines.
Deep Learning: A subset of ML using neural networks with multiple layers (hence "deep"), which excel at finding patterns in unstructured data like images, audio, and text.
Large Language Models (LLMs): A specific type of deep learning model designed to understand and generate human language. They're trained on massive text datasets to predict the next word in a sequence.
Generative Pre-trained Transformers (GPTs): A specific architecture of LLMs developed by OpenAI, using the Transformer architecture to process language more effectively.
ChatGPT: A product built on top of GPT models, specifically designed for conversational interactions, with additional safeguards and optimisations. It’s basically a chatbot app - with lots of bells and whistles.
So when you're using ChatGPT, you're interacting with just one commercial implementation of one type of language model, which is one application of deep learning, which is one approach to machine learning, which is one branch of artificial intelligence. Phew! Complex! But understanding this hierarchy already puts you ahead of 99% of people.
Try This With Your AI Tutor
Want to explore this evolution further? Try this prompt with the AI tutor you built last week:
I want to understand how AI development has evolved over time. Can you:
1. Compare and contrast symbolic AI vs neural network approaches
2. Explain why generative AI represents such a significant shift from earlier systems
3. Explain how these changes affect me as an entrepreneur in [your specific industry]
What's Next?
Tomorrow, we'll peel back the curtain on how large language models actually work – without getting lost in technical jargon.
We'll explore concepts like tokens, parameters, and context windows in ways that actually matter for your business decisions.
We'll also examine why this AI revolution is happening now.
Keep Prompting,
Kyle