This document provides effective and concrete prompt examples tailored specifically for Giselle’s Generator Nodes, leveraging various multimodal AI models including Anthropic’s Claude, Google’s Gemini, OpenAI’s GPT-4o, and Fal AI for image generation.

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Basic Examples

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1. Technical Query using GPT-4o

Role: Experienced programmer

Task:
Explain the most efficient way to extract unique elements from a Python list.

Output Format:
- Brief explanation
- Python code example demonstrating the method clearly
Why this prompt is effective:
  • Clear role definition aligns the AI’s perspective.
  • Explicit instruction and clear output expectations.
  • Specifies programming language explicitly.

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2. Image Generation with Fal AI

Role: Graphic designer

Task:
Generate a high-quality, minimalist logo for a technology startup named "QuantumLeap."

Constraints:
- Model: flux-pro/v1.1
- Color scheme: Blue and white
- Style: Modern, minimalistic

Output Format:
- PNG image (1024x1024)
Why this prompt is effective:
  • Clearly defines role and creative constraints.
  • Utilizes specific Fal AI model for high-quality outputs.
  • Precise output format suitable for immediate use.

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Advanced Examples

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1. Multimodal Analysis with Gemini

Role: Industry analyst

Task:
Analyze the attached PDF industry report and the accompanying infographic image.

Steps:
1. Summarize key findings from the PDF.
2. Identify and explain trends visible in the infographic.
3. Recommend strategic actions based on combined insights.

Constraints:
- Model: Gemini 2.5 Pro

Output Format:
- Concise summary
- Clearly explained visual trends
- Strategic recommendations as bullet points
Why this prompt is effective:
  • Clearly utilizes Gemini’s multimodal capabilities.
  • Sequential and structured instructions.
  • Explicitly states the model to ensure precise capability usage.

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2. Ethical Analysis and Content Structuring with Claude

Role: Ethics consultant

Task:
Review the provided research paper on AI surveillance technologies.

Steps:
1. Identify ethical implications.
2. Suggest practical ethical guidelines for deployment.

Constraints:
- Model: Claude 3.7 Sonnet

Output Format:
- Ethical implications listed clearly
- Practical guidelines structured as actionable points
Why this prompt is effective:
  • Aligns task specifically with Claude’s strengths in nuanced understanding.
  • Structured clearly to maximize Claude’s reasoning and analytical capabilities.

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Integrated Workflow Examples

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1. Comprehensive Market Research Workflow

Step 1 (Claude 3.7 Sonnet):
Role: Market researcher and sustainability analyst
Task: Research sustainable food packaging trends and analyze their ethical and sustainability implications.
Output Format: Analysis summary and recommendations.

Step 2 (GPT-4o):
Role: Technical writer
Task: Format the insights and recommendations into a structured industry report.
Output Format: Professional document in markdown format.

Step 3 (Fal AI flux-pro/v1.1):
Role: Graphic designer
Task: Generate a compelling visual cover image for the final report.
Constraints: Sustainability theme, professional style
Output Format: High-quality cover image (PNG, 1200x800)
Why this prompt is effective:
  • Clearly leverages strengths of multiple AI models sequentially.
  • Structured, integrated steps with explicit model assignments.
  • Produces comprehensive, ready-to-use workflow outputs.

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Key Effective Elements in Giselle Prompts

Effective Giselle Generator Node prompts typically include:
  • Explicit Role Definitions
  • Clear Constraints and Context
  • Structured Output Formats
  • Sequential or Stepwise Instructions
  • Specific Model Selections for Capabilities
For additional guidance and practical tips on creating impactful prompts, refer to tips.mdx.