Artificial intelligence, or AI, is the broad field of building computer systems that can handle tasks such as understanding language, recognising images and making predictions.
You've probably used it before opening your first chatbot. Email spam filters, translation tools and image recognition are all familiar examples. Chatbots and image generators are more visible parts of a much bigger field.
Many modern AI systems learn patterns from examples. A system trained to recognise documents might learn what an invoice looks like, then use those patterns to sort new files. Generative AI takes another route: it produces content, such as a draft email or an image, from a request.
The useful question when choosing a tool is specific: what job do you want it to do? For a long PDF, look at document handling. For a product image, look at image generation and editing. The label 'AI-powered' leaves quite a lot of the shopping homework to you.
Many modern AI systems work by learning patterns from training data, then applying those patterns to new inputs to make predictions, decisions or generated content.
Imagine training a system to sort support messages. You give it examples of billing questions, delivery problems and refund requests. During training, the model's internal settings are adjusted so its predictions better match the examples. You then test it on messages it hasn't seen before.
When someone sends a new message, the trained model processes it and predicts a useful category. That stage is called inference. A language model follows a related idea, using patterns in language to generate a response from the conversation and any information supplied to it.
Training data, the model and the instructions all matter. So does the information the tool can access when you use it. Giving an assistant the actual delivery policy can make its answer more useful than asking it to guess the policy. An important distinction: remembering something inside a conversation doesn't by itself mean the model has been retrained.
Generative AI is a type of artificial intelligence that creates content, including text, images, audio, video and code, in response to instructions or other inputs.
Ask for an email and it can draft one. Describe a scene and an image generator can produce a picture. Give a coding assistant a task and it can suggest code. The model uses patterns learned during training, together with the context available for your request, to produce its output.
The wider AI field also includes systems that classify information or make predictions. For example, a tool might identify which messages are complaints. A generative feature could then draft possible replies for someone to review.
That review matters. Generated content can sound convincing while getting details wrong. If you're creating a product description, supply the real specifications and check that the draft hasn't added an imaginary feature. A toaster with fictional Bluetooth is still a problem, however nicely the copy reads.
ChatGPT is OpenAI's AI assistant: you describe a question or task in everyday language, and it helps you work through it in a conversation.
You can use it to explain a topic, draft or revise writing, summarise information and explore ideas. Depending on the features available to your account, you can also bring files or connected tools into the task.
For example, you could paste your own rough notes and ask for a clear customer email. If the draft sounds too formal, ask for a shorter, friendlier version. You can keep refining the result in the same conversation.
You don't need to start with technical language. Explain what you're trying to achieve, give it the relevant information and review what comes back. For a practical first attempt, choose something whose quality you can judge yourself, such as rewriting a paragraph you already understand.
To use ChatGPT, open it on the web or in the app, start a chat and describe the task, the relevant context and the result you want.
For a straightforward start, sign in at chatgpt.com and choose a small task. You might want an explanation of a confusing paragraph or a clearer version of an email you've written.
Give it something concrete to work with. For example: 'Rewrite the email below for a customer whose delivery is late. Keep it under 120 words, acknowledge the delay and use only the delivery information I've provided.' Then paste your draft and the confirmed details.
Read the result and ask for a useful change: 'Make the opening warmer' or 'Cut the second paragraph.' Check names, dates, promises and factual claims before using the text. If the response invents a new delivery date, correct it and tighten the instruction.
Once you're comfortable with simple requests, add files or other available tools when they help the job. You can learn the more specialised features as you need them.
Yes, ChatGPT has a Free plan, and OpenAI also offers paid plans with different access, features and usage allowances.
Free means you can start without a monthly subscription. The exact models, tools and limits available to you can change, so check the current plan information and what your account actually offers.
Start with the work you expect to do regularly. Try drafting a couple of emails, asking for explanations or working through your own notes. If the available access handles those tasks, you already have an answer to whether you need an upgrade.
A paid plan becomes more relevant when a recurring limit or missing feature interrupts useful work. Compare that specific limitation with what the paid plan includes before subscribing.
Also keep the OpenAI API separate in your budget. API usage has its own billing; a ChatGPT subscription and API charges are different things. This is worth checking before you connect a third-party app and assume your existing subscription covers it.
An LLM, or large language model, is an AI model trained on large amounts of data to process language and generate responses from the context it receives.
It learns relationships between pieces of text called tokens. A token might be a word, part of a word or another text fragment. During generation, the model uses the available context to predict and produce more tokens.
Those learned patterns support tasks such as explaining a paragraph, summarising a document, translating text or suggesting code. For example, you could give an LLM a short technical passage and ask it to explain the main idea for a beginner.
A model is one part of the product you use. A chat application can add file handling, search, memory or other tools around it. That helps explain why two applications can feel different even when they use the same underlying model.
For choosing a tool, look at both parts: whether the model handles your task well, and whether the application gives it the information and tools it needs.
An AI agent is a system that uses AI to choose steps and use tools while working towards a goal, adjusting its approach as it gets results.
For example, an agent could research three suppliers, collect their published delivery details and prepare a comparison. It may need to search, open pages, extract information and decide whether another source is needed.
The important part is the loop: take a step, inspect what happened and choose what to do next. A fixed automation follows steps someone has already specified. Agentic systems can have more flexibility, although products use the word 'agent' in different ways.
That flexibility can also mean extra work and cost. Searches, model calls and retries all add up. If you're considering an agent, ask what it can actually access, which actions need approval and how you will check that the task finished correctly.
For a first project, choose a small job with an obvious finish line, such as producing a comparison table from named sources. It's much easier to judge whether that worked.
An AI hallucination is an output that presents incorrect or invented information as though it were a reliable answer.
It might be a made-up study, a quotation nobody said or a feature that a product doesn't actually have. The wording can be polished enough that the mistake is easy to miss.
Generative models produce plausible outputs from learned patterns and the context available to them. When that information is incomplete or ambiguous, a response can fill the gap with something that sounds reasonable but is wrong.
Imagine asking for the limits of a free AI plan. The assistant gives you a neat table, but one row describes an old plan or an invented allowance. Open the provider's current plan page and check the important details yourself. Asking the assistant to sound more certain won't improve the evidence.
You can reduce the risk by supplying relevant documents, asking for specific sources and checking that those sources support the claims. These steps help, but they don't guarantee a completely accurate answer.
A good AI prompt states the task, gives the relevant context and explains what a useful answer should look like.
Start with the job. Add the audience, the information the assistant should use and any limits that matter. If you want a particular style or structure, include a short example.
Here's a practical brief: 'Write a 100-word product description for first-time buyers. Use only these specifications: 500 ml capacity, stainless steel, screw lid. Keep the tone friendly. Don't add performance claims. Finish with three factual bullet points.'
That gives the model a task, an audience, source information, length and a format. You can then review the draft and request a targeted change, such as shortening the opening or removing a vague phrase.
If the task is unclear, ask the assistant what information it needs before it begins. For factual work, supply sources and check important claims afterwards. A clear brief can improve the response, but it can't make missing evidence appear.