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Master AIs with Structured Prompts

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Engineering Guide

Structuring Methodologies

Use these established frameworks to write requests with maximum scientific precision.

Clinical Reasoning Patterns

Chain of Thought (CoT)

Forces the AI to break down its logical thinking using clauses like: "Piensa paso a paso y justifica cada premisa antes de dictaminar la conclusión final". This mitigates logical hallucinations by 40% in mathematical and software analysis.

Few-Shot Prompting (Contextualization)

Provides concrete examples of the input-output relationship (Input/Output). Giving the AI 2-4 worked examples of the exact expected format raises the structural consistency of the final result by more than 50%.

ReAct Pattern (Reason + Act)

Special for analytical and code tasks. Forces the model to write under the hierarchy: Pensamiento -> Acción -> Observación. Helps solve problems where iterative contrasting of data is required.

The CO-STAR Framework

Designed by the Singapore government, **CO-STAR** is the gold standard for structuring any directive instruction:

  • C

    Context: Provides the background setting or initial situation.

  • O

    Objective: Defines the exact task that the model must execute.

  • S

    Style: Indicates the tone or brand guidelines (e.g., persuasive, concise, academic).

  • T

    Tone: Set the attitude of the response (e.g. professional, informal, empathetic).

  • A

    Audience: Who is it aimed at (e.g. software experts, B2B investors, 10-year-old children).

  • R

    Response: Specifies the delivery format (e.g. markdown table, JSON, ordered list).

Apply this framework wrapping your requests and you will notice a drastic change in relevance.
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