This guide explains how to optionally integrate and configure Language Models (LLMs) in GraphorLM to generate natural language responses based on retrieved information. LLM integration overview

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Overview

While GraphorLM focuses primarily on high-quality information retrieval, you can optionally add an LLM component to your pipeline to transform retrieved information into natural language responses. It’s important to note that using an LLM node is completely optional - many users can achieve their goals using just the Retrieval component as the final output.

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When to Use Retrieval Only

The Retrieval component can serve as the final stage of your RAG pipeline when:
  • You need direct access to the most relevant document chunks
  • You’re integrating with your own custom LLM solution
  • You want to implement your own response generation logic
  • You’re primarily focused on document search functionality
  • You want to optimize resource usage for LLM API calls

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When to Add an LLM Component

Consider adding the LLM component when:
  • You need natural language responses generated from retrieved content
  • You want a complete end-to-end solution without additional integration
  • Your use case requires synthesizing information across multiple documents
  • You need complex reasoning beyond simple retrieval

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The LLM Component

In GraphorLM’s Flow Builder, the LLM component takes retrieved document chunks as input and uses them to generate natural language responses: LLM component This component is where you configure which language model to use and how it should process the retrieved information to generate responses.

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Configuring the LLM Component

To set up the LLM component for your RAG pipeline:
  1. Add the LLM component to your flow
  2. Connect the output of your Retrieval component to the input of the LLM component
  3. Double-click the LLM component to open its configuration panel
  4. Configure the following settings:

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Model Selection

GraphorLM supports integration with various language models:
  • OpenAI models:
    • GPT-4o
    • GPT-4 Mini
  • Local models (when configured):
    • Various open-source models depending on your deployment
Choose the model that best balances your requirements for quality, efficiency, and speed.

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System Prompt

The system prompt defines the LLM’s behavior and instructions for generating responses. You can customize this prompt to:
  • Define the assistant’s personality and tone
  • Specify formatting requirements
  • Provide domain-specific context
  • Set boundaries for what the assistant should and shouldn’t do
Example system prompt: 
You are a helpful assistant that answers questions based on the provided context.
Always cite your sources from the context.
If you don't know the answer, say "I don't have enough information" rather than making up an answer.

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Prompt Engineering

Effective prompt design is crucial for getting optimal results from the LLM component.

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Basic Prompt Structure

GraphorLM automatically structures prompts using this general pattern:
  1. System prompt: Sets the tone and provides general instructions
  2. Retrieved context: Inserts the information retrieved from your documents
  3. User query: Adds the user’s actual question
  4. Response instructions: Provides specific guidance on how to answer

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Prompt Engineering Best Practices

  1. Be specific and clear: Provide detailed instructions about what you want
  2. Use examples: Include examples of desired responses when possible
  3. Structure matters: Order your instructions in a logical sequence
  4. Test and iterate: Refine prompts based on the responses you get

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Testing LLM Responses

To test your LLM configuration:
  1. Make sure you have a Question or Testset node connected to your Retrieval nodes
  2. Configure your LLM component
  3. Click Update Results to see the generated response
  4. Review the response for:
    • Accuracy and factual correctness
    • Adherence to the system prompt
    • Proper use of retrieved information
    • Overall usefulness and clarity
Testing LLM responses

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Next Steps

After exploring LLM integration, you may want to learn more about:

Evaluation

Measure and improve your RAG pipeline’s performance with comprehensive metrics

Retrieval

Optimize how information is retrieved to provide better context to your LLM

Integrate Workflow

Connect your RAG system to applications via REST API and MCP Server

Chunking

Refine document segmentation to improve context quality