What is a Large Language Model (LLM)?
A Large Language Model (LLM) is an AI model trained on very large volumes of text and thereby able to understand, generate, and respond to natural language requests.
DEFINITION
A Large Language Model is the technical foundation behind systems such as ChatGPT, Claude, or Gemini. The model has learned patterns from vast amounts of text and can thereby write, translate, summarise, answer, and analyse text. When you interact with an LLM, text goes in as input and the model calculates the most probable response statistically. It does not understand in the human sense, but recognises patterns in language. The size of an LLM is measured by its number of parameters: GPT-4 is estimated to have over one trillion parameters. Larger models are more capable but also more costly to run. For everyday work, a more relevant question than model size is: which model fits the use case, and how do you formulate requests for good results?
CONNECTIONS
Leadership
LLMs can relieve leaders of routine tasks: structuring feedback text, summarising meetings, or preparing decision briefs. Servant Leaders use LLMs to free more time for their team.
Agility
LLMs support agile teams in writing user stories, refining backlog items, and summarising retrospectives. Quality depends heavily on prompt quality.
Project Management
LLMs support the creation of project documents: project charters, status reports, risk analyses. That saves time but always requires substantive review by the project manager.
KEY POINTS
- LLMs are trained on vast amounts of text and generate language.
- Best-known examples: GPT-4, Claude, Gemini, Llama.
- An LLM does not understand in the human sense — it recognises language patterns.
- Model size is measured in parameters, often in billions.
- The quality of the request (prompt) determines the quality of the response.
EXAMPLE
A marketing manager quickly needs five subject lines for a campaign email. A description of target audience, offer, and desired tone is enough for the LLM. Five options appear in seconds. A selection, a tweak, and twenty minutes of writing time are saved. That is everyday life with LLMs: not a replacement for human judgement, but an accelerator for routine tasks.
MISCONCEPTIONS
Does an LLM really think?
No. An LLM calculates probabilities for word sequences based on training data. There is no consciousness, no beliefs, and no genuine understanding. The result can still be very useful.
Is a larger LLM always better for my use case?
Not necessarily. Smaller, specialised models can deliver better results for certain tasks than general large models, and are considerably cheaper to run.