Knowledge
Knowledge / RAG
Contextualized answers with relevant information.
Retrieval-Augmented Generation retrieves authorized evidence before generating an answer.
Plain definition
What it is and what problem it solves.
RAG combines retrieval and generation: before answering, it searches for relevant passages in authorized sources and adds them to the model’s context.
It reduces the work of searching across scattered sources and allows answers with backing, without turning isolated documents into a promise of absolute truth.
Architecture and limits
Technical design with explicit decisions.
Operational flow
Quality depends on the whole pipeline, not just the model.
- Sources → indexing → retrieval → reranking → context → generation.
- Embeddings and hybrid search for semantic and exact signals.
- Retrievable citations and passages to review every answer.
Controls that matter
Knowledge must respect the organization’s limits.
- Permissions applied before retrieving information.
- Freshness defined by source, owner and update cycle.
- Evaluation with real cases, coverage and evidence quality.
When it is not enough
RAG does not replace correct data, processes or expert judgment.
- It does not replace fine-tuning when a stable model behavior needs to change.
- It does not, on its own, resolve complex relationships among many entities.
- It does not remove the need to validate critical sources and answers.
Relationships
The system works as part of a platform.
Let’s turn knowledge into an operable capability.
We define the case, the sources, the limits and the evidence needed before building.