Arquitecto de Mega-Prompts para IA: Marcos de Rol, Tarea, Contexto y Formato
Arquitecto de Mega-Prompts para IA: Marcos de Rol, Tarea, Contexto y Formato
Esta herramienta opera 100% en el cliente en su navegador. Sus contraseñas, textos, documentos y datos privados nunca se transmiten a servidores externos; todo se procesa de forma segura en su memoria local.
Fórmula y Método de Cálculo
Optimal Prompt = [Persona] + [Task] + [Context/Data] + [Format/Tone Constraints]
LLMs are statistical prediction engines. If you give a vague prompt, the mathematical probability of a generic answer is high. Constraining the model with strict roles (e.g., 'Act as a Senior Python Engineer') forces it to draw from a highly specific statistical sub matrix.
Mejores Prácticas y Consejos
- Use Delimiters for Data: Never mix your instructions with your raw data. Use triple quotes ( """ ) or XML tags ( <text> ) to clearly separate the data payload from the actual command.
- Force the Format: If you want a table, do not just ask for a table. Explicitly state the columns: 'Output a Markdown table with Column 1: Feature, Column 2: Risk.'
- Implement Few Shot Prompting: If the task is complex, provide one or two perfect examples of the desired output within the prompt. The model will mathematically mimic the exact structure of your examples.