Integrating "Act as" personas and structured output formats into structured English instructions effectively transforms a natural language prompt into a rigorous software specification, minimizing ambiguity and maximizing machine readability. By treating the prompt as a pseudocode script, users can declare the AI’s identity via an initialization command (SET ROLE: Senior Python Architect) to align its reasoning engine with specific domain expertise, while simultaneously enforcing strict data contracts through explicit output directives (RETURN: JSON { key: value }). This method surrounds the core logical steps with "guardrails" that force the model to process information syntactically rather than conversationally. For coding and technology tasks, this ensures that the generated solutions not only adhere to best practices (dictated by the persona) but are also immediately parseable by downstream applications or pipelines (dictated by the output format), essentially allowing the AI to function as a deterministic API endpoint within a larger technical workflow.
Integration of Structural Commands in Structured English
| Structural Component | Structured English Command | Function & Logic | Coding/Tech Enhancement |
|---|---|---|---|
| Persona Initialization | ACT AS <Role>( ACT AS: DevOps Engineer) |
Sets the context, tone, and knowledge base. Functions as a class constructor that inherits specific domain methods. | Contextual Accuracy: Ensures code suggestions use industry-standard libraries and patterns relevant to that specific field like using boto3 for AWS DevOps. |
| Operational Constraint | CONSTRAINT <Rule>( CONSTRAINT: No external libraries) |
Defines the boundaries of the task. Acts as a conditional filter or validation check that the output must pass. | Resource Optimization: Prevents "bloated" solutions by enforcing strict environment limitations or coding standards like ensuring compatibility with legacy systems. |
| Logical Process | FOR EACH <Item> DO...( FOR EACH error: ANALYZE root_cause) |
Breaks complex tasks into iterative steps. Mimics a loop or sequence control structure to ensure comprehensive coverage. | Step-by-Step Debugging: Forces the AI to show its work (Chain-of-Thought), making it easier to verify the logic behind complex algorithms or architectural decisions. |
| Output Formatting | OUTPUT FORMAT <Type>( OUTPUT: Markdown Table) |
Dictates the visual or structural presentation. Functions as a serializer that converts internal logic into a specific view. | Documentation Speed: Instantly generates documentation (like READMEs or API specs) that is ready to copy-paste into technical reports or wikis without reformatting. |
| Data Serialization | RETURN AS JSON schema( RETURN: { "func": "name", "args": [] }) |
Enforces a strict data schema. Acts as a strongly-typed return statement, rejecting conversational filler. | Pipeline Integration: Produces machine-readable code that can be directly piped into other scripts, CI/CD pipelines, or API testing tools without manual parsing. |
Ready to transform your AI into a genius, all for Free?
Create your prompt. Writing it in your voice and style.
Click the Prompt Rocket button.
Receive your Better Prompt in seconds.
Choose your favorite favourite AI model and click to share.