🏆 IEEE ACDSA 2026 · Peer-Reviewed

Structured Prompt Engineering for Professionals

MOTIVE is the first peer-reviewed, model-agnostic framework that transforms prompt writing from an ad-hoc activity into a systematic, repeatable discipline.

6
Components
3
Tiers
5
Archetypes
10
Domains Validated

Six Components

Each letter in MOTIVE represents a distinct prompt component that addresses a specific aspect of effective communication with AI systems.

M

Motivation

  • › Why is this task being performed now?
  • › What role should the AI assume?
O

Object

  • › What is the concrete deliverable?
  • › What format should the output take?
T

Tool

  • › Which domain frameworks or methodologies should be applied?
  • › What professional standards govern this work?
I

Instruction

  • › What ordered steps should the AI follow?
  • › Are there decision points or conditional branches?
V

Variables

  • › What constraints bound the output (length, tone, format)?
  • › What must be included or excluded?
E

Evaluation

  • › What criteria determine whether the output is acceptable?
  • › What scoring scale and thresholds apply?

Three Tiers of Complexity

Not every task needs every component. MOTIVE's tiered architecture lets you scale prompt complexity to match the stakes of your work.

Tier 1

Prompt Core

Establish role, context, deliverable, and process before generation begins. The foundational layer that every MOTIVE prompt requires.

M O I
Tier 2

Precision Layer

Inject professional standards, methodological frameworks, and output constraints. Required when outputs must meet professional benchmarks.

M O T I V
Tier 3

Governance Layer

Human-in-the-loop evaluation criteria, scoring thresholds, and oversight decisions. The full MOTIVE framework for maximum quality and accountability.

M O T I V E
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Why MOTIVE?

Current prompt engineering lacks structure, repeatability, and evaluation criteria. MOTIVE addresses this gap with a six-component architecture validated through peer-reviewed research across multiple professional domains.

Five Prompt Archetypes

Common professional tasks map to recurring prompt patterns. MOTIVE identifies five baseline archetypes with domain-specific guidance.

Evidence-Based

MOTIVE is grounded in peer-reviewed research, validated through systematic evaluation across multiple domains, and continuously refined through practitioner feedback.

📚 IEEE ACDSA 2026

Peer-Reviewed · Model-Agnostic · Open Access

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Learn

Master the MOTIVE framework step by step. From core components to advanced techniques, learn how to write effective, structured prompts.
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Framework Comparison

How MOTIVE compares to other prompt engineering approaches including CRISP, RISEN, CO-STAR, and chain-of-thought methods.
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Six Components

Each letter in MOTIVE represents a distinct prompt component that addresses a specific aspect of effective communication with AI systems.
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Framework Comparison
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