Large language models: definition, value and limits
Definition
Large language models, or LLMs, are AI models trained on very large amounts of text that understand and produce language. They predict the next word each time and thereby produce coherent answers, summaries or rewrites. In automation, they are the building block that understands and categorizes free text, exactly where fixed rules fail on the variety of language.
Why the Term Matters
LLMs have moved the boundary of what a workflow can do without programming. A model understands an email, extracts fields or drafts a reply. Knowing the strengths and weaknesses, you use them deliberately and build in checks, because an LLM can sound plausible without being right. That deliberate use is the difference between a gimmick and a viable product.
When It Is Relevant
- A workflow processes free text, emails or documents.
- Structured fields should emerge from unstructured language.
- Drafts, summaries or classifications are needed.
- A language-based feature should launch as an MVP.
When Not
When an exact, recomputable answer is required, an LLM alone is risky because it can invent facts. For pure calculations, fixed lookups or strictly regulated decisions without a control layer, deterministic logic is the safe path.
Industries That Use It
- Legal-adjacent services: pre-structure long documents and mark key points, with a professional final review.
- Customer service: detect intent from free text and suggest fitting answer blocks.
- Real estate: categorize and answer listings and inquiries automatically.
- Consulting: make internal knowledge bases searchable and deliver answers with sources.
Typical Use Cases
- Understand, classify and extract fields from emails.
- Turn long texts into short summaries.
- Answer questions against an own knowledge base.
- Generate drafts for replies and documents.
- Test an LLM feature as a prototype with clear check rules.
- Set up a self-hosted MVP with approval and logging.
Decision Guide
| Signal | An LLM fits | Alternative |
|---|---|---|
| Free text must be understood | an LLM with a check step | rules and keywords |
| Exact calculation needed | rather not | deterministic logic |
| Answers from own knowledge | an LLM with retrieval | plain search |
| Feature should become a product | prototype, then MVP | one-off experiment |
Example
A team has an LLM categorize incoming requests and extract the relevant fields, all in a self-hosted n8n workflow. A validation checks the fields against allowed values and flags doubtful cases. After stable operation, it becomes an MVP with its own interface. This describes the method, not an asserted client metric.
Service Connection
An LLM often sits in a workflow automation via n8n, partly as part of AI agents. For an own language product, the path leads through MVP development.
Sources
This framing describes common practice with large language models. Model choice, check steps, data protection and hosting depend on the case, the systems and the tools used and must be checked case by case.