Memory

Series: Building Auditable LLM Systems · Part 2 of 11 An LLM system that learns from historical experience and acts online has two different jobs: produce reusable experience from past evidence; use authorized experience to make a current decision. These jobs may reuse the same engineering layers, but they require different information permissions and prompt responsibilities. The clean boundary is simple: offline processing produces memory; online control consumes it read-only. Why Mixing Offline and Online Stages Is Dangerous When extraction and execution are mixed, experimental boundaries become difficult to defend. ...

Series: Building Auditable LLM Systems · Part 8 of 11 Every fact, interpretation, and decision in an LLM system needs a clear owner. An artifact records what happened. A model may interpret evidence but cannot overwrite it. A validator determines whether derived content is suitable for a versioned asset. A controller proposes an action; a verifier and backend determine what is actually executed. Any derived information must preserve the lineage from source facts to final use. Being summarized by a model, stored in memory, or inserted into a prompt does not grant information greater factual authority. ...