Large language models (LLM): definition and use
Why the Term Matters
LLM are the engine behind the current AI surge because you work with them in natural language. That lowers the entry barrier enormously but shifts the real work to safeguarding and embedding. A model alone is not a solution yet. For a company, what counts is how the LLM connects to its own data, gets checked and enters a workflow. That is exactly what a prototype can show in days, instead of asserting it in a presentation.
When It Is Relevant
- A workflow revolves around understanding, generating or reshaping text.
- Existing knowledge should become usable through a question-and-answer interface.
- Drafts that a human checks and approves are enough.
- The case is narrow enough for a prototype with a clear review.
When Not
When exact, always identical calculations are needed or every output must be guaranteed correct, an LLM alone is the wrong basis. With strictly confidential data and no clarified operating environment, caution is due. A workshop clarifies how connection, review and data protection can be solved cleanly.
Industries That Use It
- Consulting firms: make internal knowledge bases searchable with answers that carry a source.
- Software houses: generate technical documentation and support answers from their own content.
- Real estate: structure and draft listings, contracts and enquiries.
- Education providers: learning companions that answer questions on course content.
Typical Use Cases
- An internal search that returns answers with a reference to the source document.
- An assistant that summarises long emails and drafts replies.
- A translator for technical texts with consistent terminology.
- A draft generator for quotes, reports or minutes.
- A bot that fills forms from free text and asks follow-up questions.
Decision Guide
| Signal | Term fits | Alternative |
|---|---|---|
| Task revolves around text | test an LLM with connection and review | templates for very fixed texts |
| Output must be exactly correct | only with a review step and approval | rule-based calculation |
| Always identical calculation needed | rather not | classic logic |
| Knowledge should be queryable | LLM with a connected knowledge base | classic full-text search |
Example
A consulting firm wants staff to find answers from internal methods and templates faster. In the workshop the case is cut and a prototype is built that connects an LLM to the firm's own document base and returns answers with a source reference. On real questions the team checks how often the source is right. Only then does the broader rollout follow. This describes the method, not an asserted metric.
Service Connection
The entry is the AI workshop, which ends with an LLM prototype on your own knowledge base. Then AI development and AI implementation follow.
Sources
This framing describes common practice when using large language models. Which models, connections and legal requirements apply in a given case depends on the initiative and should be checked there.