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What is it?
GPT is a type of AI model architecture developed by OpenAI, based on the transformer design. The models are trained on large text datasets and learn patterns that allow them to generate and understand fluent, coherent text.
GPT is both an architecture and a model series. GPT-4, the most recent publicly available version, is the model that underpins ChatGPT and the OpenAI API. When someone says a tool 'runs on GPT', they are referring to these OpenAI models.
Why it matters for SMEs
GPT models are the most widely used language models in office environments and business AI tools. They power much of what SMEs already use for writing, email, summarisation, and automation.
- Most popular AI tools for office work, from writing assistants to email drafting and document analysis, run directly or indirectly on GPT models via the OpenAI API.
- GPT models support function calling, which shifts them from purely generating text to controlling systems and executing workflows.
- Through the OpenAI API, GPT models can be integrated directly into existing business software, lowering the barrier for custom automation.
Understanding GPT as an architecture helps when assessing the capabilities and limits of the tools built on top of it, such as context length, training cut-off, and the differences between model versions.
How it works
GPT models operate on the transformer principle: they predict the most likely next piece of text given all preceding context. That simple core, combined with training on enormous datasets, produces models that can reason, summarise, and write coherently.
- Pre-training: the model learns language patterns from billions of text fragments.
- Instruction tuning: the model is adjusted to follow instructions and respond safely and helpfully.
- API access: you call the model from your own application via the OpenAI API.
- Prompting: you provide an instruction or question; the model generates a response based on its training and the given context.
- Tool use: via function calling or the Assistants API, the model can also execute actions beyond text output.
GPT output quality depends strongly on the quality of the instruction. A vague question produces a vague result; a precise prompt with context produces usable output.
Example in practice
Picture a recruitment agency that wants to write vacancy texts matching the tone and requirements of each client. Via the OpenAI API, the agency sends a prompt containing the job description, the desired tone, and a few example sentences. GPT-4 generates a draft that the recruiter refines and sends for approval. The agency halves the time per vacancy while keeping the style consistent per client.
Comparison and misconceptions
GPT is OpenAI's model family; Gemini is Google's model family. Both are large language models with comparable core capabilities, but they are more deeply integrated into the OpenAI and Google ecosystems respectively. It is not a choice between better and worse, but between ecosystems.
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