In short
Generative AI can draft, summarise and code in seconds, but it predicts plausible text rather than checking facts — so the value comes from a team that knows how to prompt it well and when to verify, which is exactly what our workshops build.
The basics
What it actually means
Generative AI is software that creates new content in response to an instruction you type in plain English. Ask it to draft an email, summarise a long document, write a formula or suggest images, and it produces something in seconds. The best-known tools — ChatGPT, Microsoft Copilot, Google Gemini and Claude — are all generative AI, and they're the category most workplace training focuses on because they're what your team will actually open each day.
Under the bonnet, these tools are large language models trained on huge amounts of text and other data. They work by predicting the most likely next word, pixel or line of code based on patterns they've learned. That's why the quality of what you get back depends so heavily on the quality of the prompt you put in — a skill that can be taught and practised.
What it can and can't do
Fast and fluent, but not always right
Used well, generative AI is genuinely powerful: it can draft and rewrite, summarise dense material, translate, explain code and turn a rough idea into a usable first version. It is fast, tireless and surprisingly good at the everyday writing and admin that eats into a working week.
What it can't do is guarantee accuracy. Because it predicts plausible-sounding text rather than verifying facts, it can state wrong information confidently — a problem widely known as hallucination. It also reflects bias in its training data and shouldn't be trusted with confidential information without clear rules. Knowing where to lean on it and where to check its work is the difference between a useful tool and a costly mistake.
of organisations are seeing real returns from AI despite most now using it — proof that capability, not the tool, is the bottleneck. (McKinsey, The State of AI, 2025)
How we help
How we turn generative AI into reliable capability
Knowing what generative AI is doesn't make a team good at using it. If your team is already dabbling with ChatGPT or Copilot, you'll probably recognise the symptoms: people pasting in prompts that only half-work, no shared sense of what's safe to put into a chatbot, and outputs taken at face value when they should have been checked.
That's exactly what our workshops fix — structured, hands-on practice with the generative AI tools your team already has, on the work they already do:
Prompt it properly
We teach your team how to ask for what they actually want — clear instructions, context and examples — so the tools produce usable results the first time instead of vague, generic text that needs rewriting.
Know what to trust
We train your team to spot where generative AI is strong and where it makes things up, so they verify the right outputs, keep confidential data out of public tools, and use it with confidence rather than blind faith.
Built on your real work
Sessions use your team's own documents, emails and tasks, so the skills land on day-to-day work — not generic demos — and stick once the workshop ends. Each attendee gets a certificate, and the sessions count toward CPD.
How it compares
Self-taught vs trained use of generative AI
| Self-taught | ReadyToday training | |
|---|---|---|
| Prompting | Copied prompts that half-work | Clear, repeatable technique |
| Accuracy | Outputs taken at face value | Knows what to verify |
| Data safety | No shared rules | Clear do's and don'ts |
| Examples | Generic demos | Your team's real work |
FAQ
Common questions
Is generative AI the same as ChatGPT?
What can generative AI actually be used for at work?
Why does generative AI sometimes get things wrong?
Do you train on a specific generative AI tool?
Keep exploring
Related terms
Sources & further reading
Turn generative AI into a skill your team actually has
Our hands-on workshops take your team from self-taught dabbling to confident, consistent use of the AI tools they already have — built on your real work.