Large language models: definition, value and limits
Large language models Updated
Definition
Large language models, or LLMs, are AI models trained on very large amounts of text that understand and generate language. In process automation they take over steps such as summarising, classifying, extracting from free text and drafting replies. They are powerful but need clear instructions, checked sources and control points so the output stays reliable in the flow.
Why the Term Matters
LLMs make accessible exactly the steps where pure RPA fails, understanding and phrasing language. Who places the term knows an LLM is not a knowledge store but a language tool. Provable ROI appears when the model works with the right data and the output flows cleanly into a system via RPA and integration.
When It Is Relevant
- A step needs to understand or generate free text.
- Inputs are unstructured and vary widely.
- Summarising, extracting or classifying is the bottleneck.
- Checked sources and a control point are in place.
When Not
When a statement must be exact and binding and no check sits behind it, direct LLM use is risky. For purely structured data a rule or integration suffices. And where traceability of every output is mandatory, you need tight constraints instead of free phrasing.
Industries That Use It
- Mechanical engineering: structure technical enquiries and specs from free text.
- Tax advisory: summarise client letters and prepare drafts, with a final check.
- Wholesale: generate product descriptions from master data and enquiries.
- Customer service: classify incoming messages and propose reply drafts.
Typical Use Cases
- Summarise long documents down to the decisive points.
- Extract structured fields from free text for ERP or CRM.
- Classify requests and trigger the right flow.
- Prepare reply drafts from checked sources.
- Answer content over a knowledge base with a source reference.
Decision Guide
| Signal | Large language models fit | Alternative |
|---|---|---|
| Understand or generate free text | LLM step in the flow | pure RPA is not enough |
| Binding statement | check and a vetted source | no free output |
| Structured data | no LLM needed | integration or rule |
| Full traceability | tight constraints | transparent rule |
Example
A mid-market company receives requests with long free-text descriptions. An LLM summarises them and extracts the relevant fields, RPA creates the case in the system, and integration links ERP and CRM. Uncertain points go to a person with approval. Value is proven against the baseline of prior handling. This describes the method, not an asserted client metric.
Service Connection
The entry is usually process automation with AI, the build an RPA solution, and the framing the process automation consulting.
Sources
This framing describes common mid-market practice for using large language models inside automated flows. Models, sources and data protection depend on the case, the systems and the tools and must be checked case by case.