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Text generation is one of the most powerful capabilities in Noxus, enabling you to create content, analyze text, transform information, and build intelligent assistants using state-of-the-art language models.

Text Generation Nodes

The most versatile text generation node with advanced features: Key Features:
  • Variable inputs for dynamic prompts
  • Rich text prompt editor with variable insertion
  • Model selection (cost/performance presets)
  • Streaming support for real-time output
  • Time awareness (automatic date/time injection)
  • PII detection and guardrails
  • Image URL reading capability
Use Cases:
  • Content generation (articles, emails, summaries)
  • Text transformation (rewriting, translation)
  • Question answering
  • Classification and categorization
  • Extraction with flexible outputs

OpenAI Node (Legacy)

Simple text generation for basic use cases: Key Features:
  • System prompt + user input
  • Direct model selection
  • Basic configuration
Use Cases:
  • Simple text generation
  • Legacy workflows
  • Quick prototyping
Recommendation: Use Text Generation V3 for new workflows.

Building Effective Prompts

Prompt Structure

A good prompt has clear structure:

Variable Inputs

Text Generation V3 supports variable inputs for dynamic prompts: Add Variables:
  1. Click ”+” to add input
  2. Name the variable (e.g., product_name, customer_email)
  3. Connect data or set default values
  4. Reference in prompt using {variable_name}
Example:

Prompt Engineering Best Practices

Be Specific and Clear:
Provide Examples (Few-Shot Learning):
Use Clear Delimiters:
Specify Output Format:
Set Tone and Style:

Model Selection

Cost vs Performance Trade-offs

Different models have different characteristics:

Model Presets

Text Generation V3 offers presets: Fast & Cheap:
  • GPT-3.5-turbo, Claude Haiku
  • 10x cheaper than GPT-4
  • Great for: Simple rewrites, basic Q&A, formatting
Balanced:
  • GPT-4o-mini, Claude Sonnet
  • 3x cheaper than GPT-4, nearly as good
  • Great for: Most tasks, content generation, analysis
Max Quality:
  • GPT-4, Claude Opus
  • Most expensive but best results
  • Great for: Complex reasoning, creative writing, critical analysis

Choosing the Right Model

Use Fast Models When:
  • Task is straightforward (reformatting, simple extraction)
  • High volume processing
  • Cost is a primary concern
  • Speed matters
Use Balanced Models When:
  • General-purpose text generation
  • Content creation
  • Moderate complexity
  • Good balance needed
Use Quality Models When:
  • Complex reasoning required
  • High stakes (customer-facing content)
  • Creative tasks (writing, ideation)
  • Previous models struggled
Example Strategy:

Advanced Configuration

Temperature

Controls randomness in generation:
  • 0.0: Deterministic, consistent output
  • 0.3-0.5: Focused, predictable (recommended for data extraction)
  • 0.7-0.9: Balanced creativity (recommended for content)
  • 1.0-2.0: High creativity, more diverse outputs
Use Cases:
  • Low (0.0-0.3): Classification, extraction, technical writing
  • Medium (0.5-0.8): General content, emails, summaries
  • High (0.9-2.0): Creative writing, brainstorming, diverse ideas

Max Output Tokens

Limits response length:
  • Short (100-300): Quick answers, summaries
  • Medium (500-1000): Paragraphs, emails, descriptions
  • Long (1500-4000): Articles, reports, detailed analysis
Tip: Set appropriate limits to control costs and ensure concise outputs.

Time Awareness

Automatically injects current date/time into prompts: When enabled, the model knows:
  • Current date and time
  • Day of week
  • Relative dates (“yesterday”, “next week”)
Use Cases:
  • Scheduling and calendar tasks
  • Time-sensitive content
  • Date-aware analysis

PII Detection

Automatically detects and flags personally identifiable information: Detects:
  • Names, email addresses, phone numbers
  • SSNs, credit cards, addresses
  • Sensitive personal data
Use Cases:
  • Compliance workflows
  • Data sanitization
  • Privacy protection
Behavior: Warns or blocks when PII detected (configurable).

Common Patterns

Content Generation

Blog Post Writer:
Email Generator:

Text Transformation

Rewriting:
Translation (with context):
Summarization:

Analysis and Extraction

Sentiment Analysis:
Key Information Extraction:
Classification:

Streaming Outputs

Enable streaming for real-time text generation: Benefits:
  • See results as they’re generated
  • Better user experience for long outputs
  • Early detection of issues
Use Cases:
  • Interactive chatbots
  • Real-time content generation
  • User-facing applications
Configuration: Enable “Streaming” toggle in node settings.

Error Handling

Common Issues

Empty Responses:
  • Prompt may be too restrictive
  • Model may refuse harmful requests
  • Output token limit too low
Inconsistent Outputs:
  • Temperature too high (reduce to 0.3-0.7)
  • Prompt lacks constraints
  • Need more examples in prompt
Off-Topic Responses:
  • Prompt not specific enough
  • Missing context or constraints
  • Try different model

Debugging Tips

Use Log Nodes: Insert log nodes after text generation to inspect outputs. Test with Simple Inputs: Start with basic test inputs before complex data. Iterate on Prompts: Refine prompts based on actual outputs. Try Different Models: Some models work better for specific tasks. Check Token Limits: Ensure max tokens is sufficient for expected output.

Optimization

Reduce AI model cost

Use Appropriate Models: Don’t use GPT-4 for simple tasks. Set Token Limits: Avoid unnecessarily long outputs. Cache Results: Use Memory nodes to store repeated generations. Batch When Possible: Process multiple items in one generation (if format allows).

Improve Quality

Provide More Context: More specific prompts yield better results. Use Examples: Few-shot learning improves accuracy. Iterate on Prompts: Refine based on real outputs. Choose Better Models: Upgrade to higher-quality models for critical tasks.

Improve Speed

Use Faster Models: GPT-3.5, Claude Haiku are much faster. Enable Streaming: Perceived performance improvement. Reduce Output Length: Shorter responses generate faster. Parallel Processing: Use list iteration for batch processing.

Examples

Example 1: Product Description Generator

Example 2: Customer Support Classifier

Example 3: Meeting Notes Summarizer

Next Steps

Structured Outputs

Generate JSON-structured data with schemas

Vision & Multimodal

Analyze images and mixed content

Model Selection Guide

Deep dive into choosing the right models

Text Generation Node

Complete node reference documentation

Mastering text generation unlocks the full potential of AI in your workflows. Experiment with different prompts, models, and configurations to find what works best for your use cases.