Artificial Intelligence (AI) has moved from science fiction into everyday life. Whether you’re writing an email, searching for something online, or asking your phone for directions, AI is quietly working in the background. But with so many new terms flying around, it can feel a bit overwhelming.

This post breaks down the core ideas in plain English, so you can feel confident talking about AI-without needing a computer science degree.

What Do We Mean by “AI”?

At its heart, AI is simply technology that tries to mimic human thinking. It looks for patterns, learns from information, and uses that learning to make predictions or generate useful results. That might be suggesting a movie you’ll enjoy, helping you draft a message, or spotting unusual activity on your bank account – or even to write a basic blog post!

AI isn’t magic. It’s maths, data, and clever engineering working together to solve problems.

Large Language Models (LLMs)

Large Language Models are a type of AI designed to understand and generate human‑like text. They’ve been trained on huge amounts of written material-books, articles, websites, and more-so they can recognise patterns in language.

When you ask an LLM a question, it doesn’t “know” the answer in the way a person does. Instead, it predicts what words are most likely to come next based on everything it has learned. With enough training, those predictions become surprisingly accurate and useful.

LLMs power many of the tools people use today for writing, summarising, translating, and answering questions.

Public AI vs Private AI

As AI becomes more common in workplaces, the difference between public and private AI matters more than ever.

Public AI

This is the AI most people use day‑to‑day-tools available to anyone on the internet. They’re powerful and convenient, but anything you type into them may be processed outside your organisation’s control. That can raise concerns about confidentiality and data protection.

Private AI

Private AI runs inside your organisation’s own environment. Your data stays within your control, and the AI is trained or configured specifically for your needs. It offers the benefits of AI without the risks of sending sensitive information to public systems.

Think of it like the difference between using a public library computer and using your own laptop at home.

Generative AI (GenAI)

Generative AI is a type of AI that creates things such as text, images, audio, code, and more. Instead of just analysing information, it produces new content based on what it has learned.

Examples include:

• Writing a first draft of a report
• Creating an image from a description
• Summarising long documents
• Suggesting ideas when you’re stuck

GenAI is brilliant for boosting creativity and productivity, especially when you treat it as a helpful assistant rather than a replacement for human judgement.

Agentic AI

Agentic AI is the next step forward. Instead of waiting for instructions, these systems can take action on your behalf. They can plan tasks, make decisions within set boundaries, and carry out multi‑step activities.

For example, an agentic AI could:

• Read your emails
• Identify which ones need replies
• Draft responses
• Schedule meetings
• And update your calendar

All with minimal input from you.

It’s still early days, but agentic AI has the potential to automate routine work and free up time for more meaningful tasks.

Prompt Engineering: Getting Better Results by Asking Better Questions

One of the most useful skills when working with AI is prompt engineering – a fancy term for “how to ask AI for what you want.”

You don’t need technical knowledge to do it well. It’s more about being clear, specific, and giving the AI enough context to understand your goal.

A few simple techniques make a big difference:

• Be specific – “Write a 200‑word summary for a non‑technical audience” works better than “summarise this.”
• Give examples – If you want a certain tone or style, show it.
• Set the role – for example, “Act as a friendly tutor” or “act as a security consultant” helps guide the response.
• Break big tasks into steps – AI handles structured instructions far better than vague ones.

Good prompts don’t just improve the output – they save time, reduce frustration, and help you get consistent results.

Why Learning a Little Python Can Take You Further

For everyday tasks, natural‑language prompts are enough. But when you start dealing with more complex or repetitive work, a bit of Python knowledge can unlock a whole new level of capability.

Python is one of the simplest programming languages to learn, and it pairs beautifully with AI tools. Even basic skills can help you:

• Automate repetitive tasks
  Think: cleaning up spreadsheets, renaming files, or processing logs.
• Handle larger or more complicated data
  AI can explain the code, but Python actually runs it.
• Build small tools or scripts
  Perfect for cybersecurity tasks like parsing logs, checking configurations, or analysing network data.
• Work with AI programmatically
  Instead of typing prompts manually, you can create repeatable workflows that run automatically.

You don’t need to become a developer. Even understanding the basics – variables, loops, reading files – can make you far more effective when combining AI with real‑world tasks.

Why These Concepts Matter

Understanding these basics helps you make informed decisions about how to use AI safely and effectively-whether at home or at work. It also helps you spot the opportunities AI brings, while staying aware of the risks. You should always review the responses you get from AI carefully, because it can and does make mistakes.

At some point in the future we’ll delve into each of these topics in more detail, but this is effectively a “starter for 10”.

AI isn’t here to replace people. It’s here to support us, simplify tasks, and help us work smarter. And the more you understand it, the more confident you’ll feel using it.