Common AI Parameters
Temperature
- Definition: Controls randomness in text or image generation.
- Range: 0 (less random, deterministic) to 1 (highly random, creative).
- Recommended use:
- Lower values (0–0.3) for precise, factual outputs.
- Higher values (0.7–1.0) for creative or exploratory outputs.
Top-p (Nucleus Sampling)
- Definition: Limits token selection to the smallest set whose cumulative probability exceeds a threshold (p).
- Range: Typically 0.0 to 1.0.
- Recommended use:
- Lower values (0.7–0.9) for focused, consistent outputs.
- Higher values (~1.0) for broader, diverse content generation.
Max Tokens
- Definition: Maximum number of tokens generated in a response.
- Recommended use:
- Adjust based on desired response length and API/model token limits.
- Essential for cost management and resource optimization.
Text-Specific Parameters
Frequency Penalty
- Definition: Reduces repetition by penalizing repeated tokens.
- Range: Typically 0.0 (no penalty) to 1.0 (strong penalty).
- Recommended use:
- Increase when repetitive outputs are undesirable.
Presence Penalty
- Definition: Encourages new content by penalizing tokens previously used.
- Range: Typically 0.0 (no penalty) to 1.0 (strong penalty).
- Recommended use:
- Helpful for generating more diverse text outputs.
Stop Sequences
- Definition: Tokens or phrases indicating where the model should stop generating.
- Recommended use:
- Define clearly when structured or partial outputs are required.
Image Generation Parameters
Guidance Scale
- Definition: Influences how closely the generated image follows the provided prompt.
- Range: Typically 1 (less strict adherence) to 20 (highly strict adherence).
- Recommended use:
- Lower values for exploratory, abstract outputs.
- Higher values for precise, detailed adherence to prompts.
Inference Steps
- Definition: Number of steps in the diffusion process.
- Recommended use:
- Lower values (1–4 steps with flux/schnell) for rapid prototyping.
- Higher values (~28 steps with stable-diffusion) for detailed, high-quality images.
Multimodal Parameters
Context Window
- Definition: Maximum tokens/models can “remember” or process at once.
- Typical values:
- Gemini: up to 1M tokens.
- GPT-4o: 128k tokens.
- Claude: up to 200k tokens.
- Recommended use:
- Use larger context windows for extensive documents, multimodal data analysis, and tasks requiring detailed understanding.
Input Modalities
- Definition: Types of inputs supported by the model (text, images, audio, video).
- Models:
- Gemini 2.5 Pro and GPT-4o support extensive multimodal inputs.
- Choose models based on required input modalities.
Web-Search Parameters
Grounding
- Definition: Enables the model to incorporate real-time web-search results into generated responses.
- Recommended use:
- Enable for up-to-date, fact-based research tasks or informational queries.
Practical Recommendations for Giselle
- Experimentation and Adjustment: Regularly adjust parameters based on task-specific results.
- Node Integration: Use parameters strategically across chained nodes to maximize workflow effectiveness.
- Document Settings Clearly: Clearly document chosen parameter settings within your Giselle workflow for team clarity and reproducibility.