What conventional RAG does well.
Retrieval-augmented generation is useful for locating passages in document collections and providing them to a language model. It can improve relevance and reduce reliance on the model's general training data.
For many reference questions, a well-designed retrieval system is exactly the right tool. The limitation appears when the organisation needs more than the text of a document.
What a living memory adds.
Macrosona connects documents with the conversations, decisions and experience that explain how knowledge was interpreted and used. It treats memory as something that develops, not just a set of chunks waiting to be retrieved.
- Decision rationale and expert judgement alongside formal material.
- Distinct evidence, knowledge and experience records.
- Context boundaries across clients, projects and teams.
- Lineage, review status and change over time.
Use retrieval inside a wider memory system.
Macrosona still uses retrieval. The difference is what it retrieves and how that material is governed. Search becomes one capability inside an organisational memory layer rather than the complete knowledge strategy.