Principle:Diagram of thought Diagram of thought System Prompt Configuration
| Knowledge Sources | |
|---|---|
| Domains | Reasoning, Prompt_Engineering, LLM_Configuration |
| Last Updated | 2026-02-14 04:30 GMT |
Overview
System Prompt Configuration is the technique of conditioning a large language model's behavior through a structured system prompt that defines role-based reasoning protocols, enabling the model to perform multi-perspective iterative reasoning without any external controller or library dependency.
Description
In the Diagram of Thought (DoT) framework, system prompt configuration is the foundational step that transforms a general-purpose LLM into a structured iterative reasoner capable of constructing Directed Acyclic Graphs (DAGs) of reasoning. Rather than relying on external orchestration, multi-agent setups, or specialized search algorithms, DoT leverages in-context learning to establish multiple internal roles within a single model through a carefully crafted system prompt.
The system prompt defines three distinct roles -- proposer, critic, and summarizer -- each delineated by XML tags (<proposer>, <critic>, <summarizer>). These role tokens act as behavioral contracts: when the model generates text within a particular tag, it adopts the corresponding persona and objectives. The proposer advances the reasoning by generating candidate steps, the critic evaluates those steps for logical soundness and accuracy, and the summarizer synthesizes validated propositions into a coherent conclusion.
This approach draws on the broader principle of role prompting, where a system-level instruction establishes persistent behavioral patterns throughout a session. In DoT, role prompting is elevated from a stylistic tool to a structural mechanism: the alternation between roles produces a self-correcting reasoning loop that mirrors formal verification processes. The system prompt also specifies a process flow (propose, critique, assess, repeat) and formatting guidelines (clarity, logical progression, correct tag usage, natural language explanations) that collectively ensure the model's output is both well-structured and auditable.
Because the entire configuration resides in the system prompt, it is library-agnostic and API-agnostic. Any LLM endpoint that supports a system message parameter (OpenAI, Anthropic, open-source inference servers, etc.) can host a DoT-configured session without modification to the underlying model or tooling.
Usage
System prompt configuration is applied at session initialization, before any reasoning begins. It is the prerequisite for all subsequent DoT reasoning activity. The prompt must be loaded as the system message (or equivalent) in the very first API call of the session. Once loaded, the LLM is ready to accept a problem statement and begin the iterative propose-critique-summarize cycle. No additional configuration or middleware is required between loading the prompt and commencing reasoning.
Theoretical Basis
The theoretical foundation for system prompt configuration in DoT rests on three pillars:
In-context learning and role tokens. The DoT paper (arXiv:2409.10038) introduces the concept of learned role tokens -- the XML tags <proposer>, <critic>, and <summarizer> serve as lightweight control signals that steer the model's generation behavior. Because modern LLMs have been trained on vast corpora containing XML-structured text and role-play patterns, these tokens reliably activate distinct behavioral modes without any fine-tuning. The system prompt establishes a behavioral contract that persists across the entire generation, ensuring that each role token triggers the appropriate reasoning style.
Internalized controller paradigm. Traditional graph-based reasoning methods (Tree-of-Thought, Graph-of-Thought) rely on external controllers that manage search, branching, and backtracking. DoT eliminates this external dependency by encoding the control logic directly into the system prompt. The process flow instructions (propose, critique, assess, repeat until the summarizer confirms completeness) serve as an internalized controller that the model follows autoregressively. This makes the system controller-light while preserving the structural advantages of DAG-based reasoning.
Formal guarantees via the prompt structure. The system prompt's formatting guidelines and role definitions are designed to produce output that can be interpreted through the lens of topos theory as described in the paper. Validated propositions correspond to subobjects in a mathematical space, and the summarizer's synthesis corresponds to a colimit -- a universal construction that optimally combines all validated evidence. The system prompt configuration ensures that the model's output conforms to the structural requirements needed for these formal guarantees to hold.