Large Language Model (LLM) – Definition, Use Cases and Best Practices at a Glance
A large language model is an AI model trained on vast text corpora that can understand, generate and complete natural language.
What is a large language model (LLM)? Definition and use cases
Models such as GPT, Claude or Llama power modern chatbots, assistants and knowledge tools. They answer questions, draft text and support code – often combined with retrieval (RAG) for company-specific answers.
This glossary entry for Large Language Model (LLM) gives you a clear Definition, practical Use Cases and Best Practices at a glance – with examples, pros and cons, and FAQs.
What is Large Language Model (LLM)?
- Large Language Model (LLM) – A large language model is an AI model trained on vast text corpora that can understand, generate and complete natural language.
A large language model (LLM) is a neural network with billions of parameters trained on large text datasets. It predicts the next token and thereby generates coherent language. LLMs are accessed via APIs or run as open-source models on your infrastructure.
For domain-specific tasks they are often paired with RAG or fine-tuning.
How does Large Language Model (LLM) work?
Input (prompt) is tokenised; the transformer predicts continuations token by token. Prompt engineering and optional RAG steer behaviour. In production, guardrails, logging and data-protection rules apply.
Practical Examples
An internal assistant answers HR policy questions using RAG over the company wiki.
Groenewold IT Solutions integrates LLMs into knowledge bases and chatbots with clear data boundaries.
Typical Use Cases
Chatbots and assistants
Knowledge bases with RAG
Summarisation and drafting
Code assistance
Advantages and Disadvantages
Advantages
- Versatile without training per task
- Strong language quality
- Fast integration via APIs
Disadvantages
- Possible hallucinations
- Cost and latency at scale
- Data protection for external APIs
Frequently Asked Questions about Large Language Model (LLM)
What is the difference between an LLM and RAG?
The LLM generates language; RAG retrieves relevant documents first and adds them to the prompt so answers use your knowledge.
Can an LLM run on-premises?
Yes. Open-source models can run on your servers or private cloud for tighter data control.
Direct next steps
If you want to apply or evaluate Large Language Model (LLM) in a real project, start with these transactional pages:
Large Language Model (LLM) in the Context of Modern IT Projects
What this glossary entry gives you
This page gives a concise definition of Large Language Model (LLM). You also get practical use cases and best practices at a glance.
You can use it to evaluate the technology for your next project. Large Language Model (LLM) sits in the domain of AI & Data. It plays a significant role across many IT projects.
Look beyond isolated technical merits
When you judge whether Large Language Model (LLM) is the right fit, look beyond isolated technical merits. You should weigh the full project context.
Consider the following factors:
- Existing team expertise
- Current infrastructure
- Long-term maintainability
- Total cost of ownership (TCO)
Drawing on our experience from over 250 software projects, we have found that correctly positioning a technology or methodology within the broader project context often matters more than its isolated strengths.
How we help you decide
At Groenewold IT Solutions, we have worked with Large Language Model (LLM) across multiple client engagements. We know its advantages and the typical challenges during adoption.
If you are unsure whether Large Language Model (LLM) suits your requirements, ask us for an honest, no-obligation assessment. We analyze your situation. We recommend the approach that delivers the most value. We may suggest an alternative solution if that fits better.
Where to go next
For more terms in AI & Data and related topics, open our IT Glossary.
For concrete applications, costs and processes, use our service pages and topic pages. There you will see many of the concepts from this entry applied in practice.
Related Terms
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