Basic LLM Chain

Basic LLM Chain

What It Does

The Basic LLM Chain node adds AI intelligence to your workflows. Think of it as having a smart assistant that can read, analyze, and respond to any text you give it. Whether you want to summarize articles, extract key information, or generate content, this node makes it simple.

What Goes In, What Comes Out

Input

Name	Type	Description	Required	Default
llm	LLM Connection	Your AI model (like OpenAI GPT-4)	Yes	-
prompt	Text	Instructions for the AI	Yes	-
input_text	Text	Content to process	No	-
temperature	Number	How creative the AI should be (0-1)	No	0.7
max_tokens	Number	Maximum response length	No	1000

Output

Name	Type	Description
response	Text	AI-generated response
tokens_used	Number	How much AI processing was used
processing_time	Number	Time taken in milliseconds

Real-World Examples

📊 Summarize Articles: Extract key points from news articles or blog posts

• Input: Long article text
• Output: 3-sentence summary

🔍 Extract Information: Pull specific details from product pages

• Input: Product description
• Output: Features, price, specifications

✍️ Generate Content: Create social media posts from website content

• Input: Company news
• Output: Tweet-ready content

How It Works

flowchart LR
    A[📄 Web Content] --> B[🤖 Basic LLM Chain]
    B --> C[✨ AI Response]

    style A fill:#e3f2fd
    style B fill:#fff3e0
    style C fill:#e8f5e8

1. Input: You provide text content and instructions
2. Processing: AI analyzes and processes your content
3. Output: Get intelligent responses, summaries, or generated content

Quick Start Example

Goal: Summarize a product page

Setup:

• Choose OpenAI GPT-4 as your AI model
• Write prompt: “Summarize this product in 2 sentences”
• Set temperature to 0.3 for consistent results

Result: Get a clear, concise product summary perfect for comparison shopping or quick reviews.

Configuration Tips

Essential Settings

• Temperature: Lower (0.1-0.3) for consistent results, higher (0.7-0.9) for creative content
• Max Tokens: Start with 500 for summaries, 1000+ for detailed analysis
• Prompt: Be specific about what you want - “Summarize in 3 bullet points” works better than “Summarize this”

Simple Setup Guide

For Summarizing Content 📝

• Set Temperature to 0.3 (keeps summaries consistent)
• Set Max Tokens to 300 (perfect length for summaries)
• Use prompts like: “Summarize this in 3 key points”

For Extracting Information 🔍

• Set Temperature to 0.1 (very precise extraction)
• Set Max Tokens to 500 (enough space for details)
• Use prompts like: “Extract the title, price, and main features”

Browser Compatibility

Works in all major browsers:

• ✅ Chrome: Full support
• ✅ Firefox: Full support
• ⚠️ Safari: Limited caching
• ✅ Edge: Full support

Privacy & Security

• 🔒 Your data stays secure: API keys stored safely in browser
• 🌐 Encrypted connections: All AI processing uses secure HTTPS
• 🚫 No data retention: Sensitive information isn’t stored permanently

Step-by-Step Workflow

1. Extract Content

Use Get All Text From Link to grab content from any webpage

2. Process with AI

Connect to Basic LLM Chain with your instructions

3. Get Results

Receive intelligent analysis, summaries, or generated content

4. Use the Output

Save to file, send to another node, or display results

Try It Yourself

Example 1: Article Summarizer

What you’ll build: Automatically summarize any news article

Workflow:

Get All Text From Link → Basic LLM Chain → Download As File

Configuration:

• Prompt: “Summarize this article in 3 bullet points highlighting the main news: {content}”
• Temperature: 0.3 (for consistent results)
• Max Tokens: 300

Result: Get clean, consistent summaries perfect for daily news briefings.

Example 2: Product Feature Extractor

What you’ll build: Extract key features from any product page

Workflow:

Get HTML From Link → Basic LLM Chain → Edit Fields

Configuration:

• Prompt: “Extract from this product page: name, price, top 3 features. Format as: Name: [name] | Price: [price] | Features: [feature1, feature2, feature3]”
• Temperature: 0.1 (for accurate extraction)

Result: Structured product data ready for comparison or database storage.

🔍 Advanced Example: Multi-Language Content

What you’ll build: Translate and summarize foreign language content

Configuration:

• Prompt: “First translate this to English, then summarize in 2 sentences: {content}”
• Temperature: 0.4

Use case: Monitor international news or competitor content in other languages.

Best Practices

✅ Do This

• Be specific in prompts: “Extract 3 main features” vs “Tell me about this”
• Use low temperature (0.1-0.3) for data extraction
• Use higher temperature (0.7-0.9) for creative content
• Test with sample content before running on multiple pages

❌ Avoid This

• Vague prompts that confuse the AI
• Very high token limits (wastes resources)
• Processing extremely long content without chunking

Troubleshooting

🚫 “Too Many Requests” Error

Problem: Getting rate limit errors Solution: Add delays between requests or upgrade your AI service plan

🎲 Inconsistent Results

Problem: AI gives different answers to the same content Solution: Lower temperature to 0.1-0.3 for more consistent results

⏱️ Slow Processing

Problem: AI takes too long to respond Solution: Reduce max_tokens or break large content into smaller chunks

📝 Wrong Output Format

Problem: AI doesn’t follow your format instructions Solution: Be more specific in prompts: “Format as: Title: [title] | Summary: [summary]“

Limitations to Know

• Content Size: Very large documents may need to be split into smaller pieces
• Processing Time: AI responses typically take 2-10 seconds
• Internet Required: Needs connection to AI service (unless using local models)
• Costs: Cloud AI services charge per use (tokens)

Related Nodes

🔄 Similar Nodes

• Q&A Node: Better for specific questions about content
• RAG Node: Better when you need to search through lots of documents

🔗 Works Great With

• Get All Text From Link: Grabs content from web pages
• Edit Fields: Cleans up and formats AI responses
• Download As File: Saves results to your computer

🛠️ Required Setup

• Ollama: For local AI processing (privacy-focused)
• WbeLLM: For cloud AI services (OpenAI, Anthropic, etc.)

What’s Next?

🌱 New to AI Workflows?

Start with our AI Workflow Builder Tutorial

🚀 Ready for More?

• Try Q&A Node for question-answering
• Explore RAG Node for document search
• Check out real-world examples

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💡 Pro Tip: Start simple with article summarization, then gradually build more complex workflows as you get comfortable with AI processing.