​

Node Description

The LLM node allows users to input specific instructions, which are processed by a language model to generate text, provide analysis, or execute defined tasks.

​

Key Features

• Customizable Instructions: Tailor the model’s response with detailed instructions and dynamic variables (e.g., {{myVariable}}).
• Adjustable AI Parameters: Fine-tune model behavior by configuring temperature, token limits, and more.
• Versatile Applications: Use for text generation, analysis, summarization, and other NLP tasks.

​

Node Inputs

​

Required Fields

Instructions
Provide specific instructions to the model. Use dynamic variables ({{myVariable}}) to personalize input.
Examples:

• "Write a story about F1 driver loosing the championship for this driver {{driver}} with the team {{team}}"
• "Summarize the provided text in 100 words."

​

Optional Fields

System Message (Optional)
Set the context for the AI model to operate under.
Example: "You are a data analyst specializing in market trends."

​

AI Settings

​

Model

Choose the LLM to use.
Example: "OPENAI - gpt-4o-2024-05-13"

​

Temperature

Controls the randomness of the output:

• 0.2: Deterministic and focused.
• 0.6: Balanced between creativity and precision (default).
• 0.8: More creative and exploratory.

​

Max Output Tokens

Defines the length of the response.
Example: 1000 tokens.

​

Top K

Limits the selection to the top K most likely tokens.
Example: 40

​

Top P

Adjusts probability for token selection.
Default: 1 (uses full probability distribution).

​

Node Output

​

Output

The LLM’s response based on the provided instructions.

​

Example Output:

{
  "summary": "AI tools streamline workflows and enhance productivity in various industries.",
  "keywords": ["AI tools", "productivity", "workflows"]
}

​

Reflection Agent Integration

​

Purpose

The Reflection Agent works as a guide to refine AI-generated content by providing constructive feedback.

​

Best Practices

• Start with broad guidance and refine with focused instructions.
• Ensure instructions are clear and do not contradict.
• Use feedback iteratively for improved content quality.

​

Example Usage

1. Content Analysis
   Instruction: "Analyze if the text aligns with the user's search intent."

2. SEO Suggestions
   Instruction: "Recommend additional keywords for optimizing this content for search engines."

3. Structural Review
   Instruction: "Check if the content is well-structured with clear headings and subheadings."

The LLM node is adaptable for a wide range of use cases, making it an essential tool for generating, refining, and analyzing content.