Series: Building Auditable LLM Systems · Part 1 of 11 A small LLM prototype can often be built with one prompt. Put the current state, a few examples, the output format, and an instruction into a single block of text, then see whether the model returns a plausible answer. That is enough for a smoke test. It is not enough for a system that must be reproduced, audited, or trusted to affect an external process. ...
Series: Building Auditable LLM Systems · Part 3 of 11 An LLM schema does more than list fields. It defines the boundary between facts, model judgment, and feedback. Two principles are especially important: Label whether each value comes from deterministic computation or model inference. Store every model judgment with the explicit rationale produced for that decision. The first principle protects the factual boundary. The second preserves the model’s decision-time account of its judgment. ...
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. ...