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 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 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 4 of 11 Separate scenario design for an LLM decision system into two layers. The first layer generates worlds that satisfy the physical, operational, and lifecycle constraints of the domain. The second layer organizes those worlds into named task families that define what the model should learn, where it should be tested, and which claims the evaluation can support. These layers answer different questions: ...
Series: Building Auditable LLM Systems · Part 5 of 11 The prompt is the model’s decision interface, not a place to concatenate everything the system knows. A well-designed prompt follows the order of the decision. It explains the task, the action boundary, the current facts, relevant history or external experience, the requested justification, and the output contract. Keep content with different sources and responsibilities in distinct blocks. This structure makes it possible to know what the model actually saw, why it made a judgment, and whether two methods were compared under the same prompt contract. ...
Series: Building Auditable LLM Systems · Part 6 of 11 In an LLM–algorithm system, evidence tools form a read-only interface to environment state, algorithm outputs, and mechanism evidence. These tools retrieve information and perform deterministic computation. They do not generate natural-language conclusions, recommend actions, or execute state changes. The model combines structured evidence into an interpretation and a bounded proposal; a verifier and deterministic backend retain authority over legality and execution. ...
Series: Building Auditable LLM Systems · Part 7 of 11 Begin the design of an LLM decision system with its action space. Before designing prompts, tools, memory, or workflow logic, answer a simpler question: What is the model actually allowed to change? Once that boundary is fixed, the rest of the architecture can be derived from it: What may the model change? -> What must it know to choose that action? -> How can the system verify that the action follows from that evidence? -> How is the model prevented from taking other actions? -> Who validates and executes the proposal? -> How are consequences returned as feedback? Schemas, prompts, tools, workflows, and memory all serve this chain from information to action and consequence. ...
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. ...
Series: Building Auditable LLM Systems · Part 9 of 11 Several LLM methods can share infrastructure. Share only the components whose meaning, timing, and tests remain unchanged. Sending model requests, validating output structure, recording traces, and executing legal actions are often generic. The meaning of evidence, the semantics of an action, the way experience is learned, and the definition of success are usually method-specific. Do not decide the boundary from code duplication alone. Ask whether sharing changes or conceals the meaning of the method. ...
Series: Building Auditable LLM Systems · Part 10 of 11 An online evaluation is valid only when every decision point begins from a shared boundary on visible information. An LLM, a deterministic rule, and an online planner may use different data formats and computations. They must still base their decisions on semantically equivalent current state, observed history, and future-information boundaries. A method that sees more information may remain useful as a hindsight upper bound. It is no longer a fair online comparison and cannot by itself prove that the LLM made the wrong decision. ...