To design prompt architecture modularly one must adopt a component-based approach known as Prompt Templating. Example: treating prompts as structured artifacts that occupy a middle ground between loose natural language and rigid executable code. This involves deconstructing a prompt into distinct, reusable blocks such as personas, context, constraints, and few shot examples, which are loosely coupled and dynamically assembled at runtime. By separating the static logic (instructions and formatting rules) from the dynamic data (user input and variable context), developers can treat prompts like configuration files or markup languages. This architecture allows for version control, A/B testing of specific modules like swapping out an "Output Formatter" module without changing the "Core Instruction," and programmatic optimization, ensuring the LLM receives a highly structured input that is readable by humans but compiles into a deterministic set of tokens for the machine.
Modular Prompt Architecture Components
| Module Component | Function | The "Middle Ground" Implementation |
|---|---|---|
| Persona Wrapper | Defines the role, tone, and domain expertise. |
Not just:
"Act as a lawyer." Not code: class Lawyer(Role):
Modular: A reusable text block injection: {{LEGAL_EXPERT_PERSONA_V2}}
containing specific behavioral nuances.
|
| Context Container | Provides static background data or constraints necessary for the specific task. |
Not just:
Pasting a whole document. Not code: db.query(context)
Modular: Dynamic variable insertion {{RELEVANT_CASE_HISTORY}}
populated by a vector search before the prompt is finalized.
|
| Instruction Core | The primary verb or task description (the immutable logic). |
Not just:
"Summarize this." Not code: def summarize(text):
Modular: A standardized template: [TASK: ANALYZE_SENTIMENT] [TARGET: {{USER_INPUT}}] [DEPTH: DETAILED]
designed to be interchangeable across different models.
|
| Few-Shot Library | A repository of input-output pairs to guide the model's logic. |
Not just:
Writing an example randomly. Not code: Unit tests. Modular: A selectable array {{FEW_SHOT_EXAMPLES_FINANCE}}
that injects 3-5 specific examples relevant to the current input category.
|
| Output Guardrails | Enforces specific formatting schemas (JSON, XML, Markdown). |
Not just:
"Give me a list." Not code: return json.dumps(data)
Modular: A schema definition block appended to the end: Response must strictly adhere to the following TypeSpec: {{JSON_SCHEMA_V1}}
.
|
| Sanitization Layer | Pre-instructions to prevent jailbreaking or hallucinations. |
Not just:
"Don't be bad." Not code: Input validation logic. Modular: A security header {{SAFETY_SYSTEM_PROMPT}}
prepended to every prompt call to ensure compliance without rewriting rules every time.
|
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