Retrieval: definition and role for provable AI
Why the Term Matters
A language model knows nothing about a company's current content and tends to guess without a source. Retrieval closes this gap by delivering the right passages for the question. It is the most effective lever against invented answers and the basis for provable information. A prototype quickly shows whether the existing source returns the right hits or first needs ordering.
When It Is Relevant
- Answers should rest on company knowledge and be provable.
- The knowledge changes, a static model would soon be outdated.
- Users expect a reference to the source of a statement.
- The model should not guess when it lacks the right context.
When Not
If a task is purely creative or the model's general knowledge is enough, retrieval adds no value. If the knowledge source is very small and fits fully into the request, a costly search step is redundant. Retrieval pays off once a lot of changing knowledge should flow provably into answers.
Industries That Use It
- Technical support: answers from extensive manuals with a reference to the exact passage.
- Legal departments: information from current policies and contracts, every statement provably backed.
- Pharma and medical devices: answers from approved documents, because free interpretation is not acceptable.
- Public transport: information from current timetable and tariff data instead of outdated model knowledge.
Typical Use Cases
- A support assistant finds the right manual passage and quotes it.
- An internal knowledge bot references the source document on every answer.
- A research assistant finds relevant passages across many documents.
- On no hit the assistant says openly that it finds nothing.
- Current content is connected so answers do not go stale.
Decision Guide
| Signal | Retrieval fits | Alternative |
|---|---|---|
| Answers should be provable | find and pass the right passages | free model only with control |
| Knowledge changes constantly | connect to a current source | static model answer |
| Source very small | rather not | put knowledge directly in the request |
| Purely creative task | rather not | model without extras |
Example
A public transport operator wants to answer passenger questions automatically without risking outdated information. In the workshop a prototype emerges that finds the right passages from current timetable and tariff data for each question and answers with a reference. On real questions it becomes clear where the search hits safely and where it needs adjusting. This describes the method, not an asserted client metric.
Service Connection
The right entry is the AI workshop, then AI development and AI implementation.
Sources
This framing describes common practice around retrieval in AI applications. How well the search hits depends on the source and its preparation and should be checked on the real case.