Blog series · 11 parts
Reliable LLM systems require more than a capable model and a carefully written prompt. They need explicit boundaries around facts, tools, memory, actions, execution, and feedback.
This series develops those boundaries one component at a time. Each article addresses one design problem and stands on its own; together, the eleven parts form a practical architecture for building LLM decision systems that can be validated, replayed, and audited.
Contents
Why an LLM decision system needs five separable layers rather than one increasingly large prompt.
A clean LLM architecture separates the production of reusable experience from its read-only use during online decisions.
A schema preserves the boundary between computed facts, model judgments, explicit rationale, and external feedback.
Reliable evaluation separates plausible executable environments from meaningful LLM task distributions.
A prompt follows the order of an actual decision and keeps facts, memory, tools, actions, and runtime logic in separate layers.
Evidence tools expose structured facts and deterministic calculations without making decisions on the model's behalf.
Define what an LLM is allowed to change before designing its schemas, prompts, tools, workflows, and memory.
Every fact, interpretation, proposal, and outcome in an LLM system needs a clear owner and a traceable evidence lineage.
Share code across LLM methods only when its meaning, lifecycle, failure handling, and tests remain unchanged.
Fair online comparison requires every method to decide from the same semantic snapshot and the same boundary on future information.
A strong teacher result is a valid target only when the model can observe its basis and execute the same action.