Retrieval: definition and role in the assistant
Definition
Retrieval is the targeted fetching of the right knowledge passages for a question before the assistant answers. Instead of relying on the model's memory, the system searches the approved knowledge base for the relevant passages and passes them to the model. That produces an answer grounded in your real content whose origin can be shown. Retrieval is the core of a citable company brain.
Why the Term Matters
A model alone does not know your company knowledge and would guess. Retrieval closes that gap: it fetches the fitting passages for the question from your knowledge base so the assistant answers from checked sources and shows the origin. That lowers the risk of invented answers and keeps the assistant current without retraining the model. Who understands the term sees why a good company brain is above all good retrieval.
When It Is Relevant
- Answers should come from your own, approved knowledge.
- The origin of a claim should be traceable.
- Content changes without retraining the model.
- Rights must decide which passages may be found at all.
When Not
For general-knowledge questions unrelated to your content, retrieval is not needed. When only a single, short document is used, the direct route is simpler. The value arises once an assistant should answer citably from a larger, maintained knowledge base.
Industries That Use It
- IT and software: a support assistant that finds and cites the right doc passage.
- Real estate: information from property files with a clear passage.
- Consulting: analyses that bind every claim to a source in the knowledge base.
- Health: citable answers from guidelines instead of model memory.
Typical Use Cases
- The company brain fetches the relevant passages for the question and shows the origin.
- The personal assistant draws only passages the user is allowed to see.
- Only the fitting excerpts go to the model, not whole documents.
- Updated content takes effect immediately, without retraining the model.
- If retrieval finds nothing fitting, the assistant answers cautiously rather than guessing.
Decision Guide
| Signal | Retrieval fits | Alternative |
|---|---|---|
| Answer should come from own knowledge | fetch fitting passages for the question | model memory guesses without grounding |
| Origin should be citable | pass the passage along | avoid an answer without a source |
| Content changes often | knowledge base instead of retraining | retraining the model is expensive |
| Pure general knowledge | often not needed | the model answers directly |
Example
A team connects its company brain to manuals and policies through retrieval. For each question the system fetches the fitting passages, passes them to the model and shows the origin. If a policy changes, it takes effect immediately, without retraining. If no passage is found, the assistant holds back. The example describes the method, not an asserted hit rate.
Service Connection
Retrieval is the heart of a citable company brain and feeds a personal AI assistant with answers from your own knowledge.
Sources
This framing describes common practice around retrieval. How well it works in a given case depends on preparation, rights and the quality of the knowledge base, and must be checked in context.
