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The prompt-tune command automatically generates domain-specific prompts by analyzing your input documents. This creates prompts that are better suited to your specific use case than the default generic prompts.

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Usage

graphrag prompt-tune [OPTIONS]

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Options

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--root
string
default:"current directory"
The project root directory.Aliases: -r
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--domain
string
The domain your input data is related to. For example: “space science”, “microbiology”, “environmental news”, “legal documents”.If not specified, the domain will be automatically inferred from the input data.
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--output
string
default:"prompts"
The directory to save generated prompts to, relative to the project root directory.Aliases: -o
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--selection-method
string
default:"random"
The text chunk selection method for prompt generation.Available methods:
  • random - Randomly select chunks (fastest)
  • top - Select top chunks by order
  • auto - Automatically select diverse chunks using embeddings (best quality)
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--limit
integer
default:"15"
The number of documents to load when --selection-method is random or top.
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--n-subset-max
integer
default:"300"
The number of text chunks to embed when --selection-method=auto. Higher values provide more diversity but take longer.
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--k
integer
default:"15"
The maximum number of documents to select from each centroid when --selection-method=auto.
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--max-tokens
integer
default:"2000"
The maximum token count for prompt generation examples.
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--min-examples-required
integer
default:"2"
The minimum number of examples to generate/include in the entity extraction prompt.
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--chunk-size
integer
default:"1200"
The size of each example text chunk in tokens. Overrides chunking.size in the configuration file.
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--overlap
integer
default:"100"
The overlap size for chunking documents in tokens. Overrides chunking.overlap in the configuration file.
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--language
string
The primary language used for inputs and outputs in GraphRAG prompts.Examples: “English”, “Spanish”, “French”, “Japanese”If not specified, defaults to English.
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--discover-entity-types
boolean
default:"true"
Discover and extract unspecified entity types from the data.Use --no-discover-entity-types to disable and only use predefined entity types.
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--verbose
boolean
default:"false"
Run the prompt tuning pipeline with verbose logging.Aliases: -v

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Examples

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Basic prompt tuning

Generate prompts with automatic domain inference: 
graphrag prompt-tune

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Specify domain

graphrag prompt-tune --domain "medical research"

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Use auto selection for better quality

graphrag prompt-tune --selection-method auto
The auto selection method uses embeddings to select diverse, representative samples from your documents.

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Customize output directory

graphrag prompt-tune --output custom_prompts

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Multilingual prompt generation

graphrag prompt-tune --language "Spanish" --domain "noticias políticas"

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Increase sample diversity

graphrag prompt-tune \
  --selection-method auto \
  --n-subset-max 500 \
  --k 20

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Use predefined entity types only

graphrag prompt-tune --no-discover-entity-types

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Custom chunking parameters

graphrag prompt-tune \
  --chunk-size 800 \
  --overlap 200

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Verbose output

graphrag prompt-tune --verbose

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Generated prompts

The prompt-tune command generates three customized prompt files:

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extract_graph.txt

Prompt for extracting entities and relationships from text. This is customized with:
  • Domain-specific entity types discovered from your data
  • Example extractions from your actual documents
  • Domain context and terminology

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summarize_descriptions.txt

Prompt for summarizing entity descriptions. Customized with:
  • Domain-specific summarization guidelines
  • Examples from your data

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community_report.txt

Prompt for generating community reports. Customized with:
  • Domain-appropriate report structure
  • Relevant analysis dimensions for your domain

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How it works

  1. Sample selection: Selects representative text chunks from your documents using the specified selection method
  2. Domain analysis: Analyzes the samples to understand domain characteristics and terminology
  3. Entity discovery: Identifies domain-specific entity types present in your data
  4. Example generation: Creates few-shot examples by extracting entities from sample chunks
  5. Prompt creation: Generates prompts incorporating discovered entity types and examples

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Selection methods

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Random selection

  • Fastest method
  • Randomly samples chunks from documents
  • Good for homogeneous datasets
  • Use --limit to control sample size

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Top selection

  • Selects first N chunks in order
  • Fast and deterministic
  • Good when early content is representative
  • Uses embeddings to find diverse, representative samples
  • Creates embeddings for up to --n-subset-max chunks
  • Clusters chunks and selects up to --k from each cluster
  • Best quality but slower due to embedding generation
  • Recommended for heterogeneous datasets

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Best practices

  1. Use auto selection: For best results, use --selection-method auto with adequate sample size
  2. Specify domain: Providing a domain helps generate more focused prompts
  3. Diverse samples: Ensure your input documents are representative of your full dataset
  4. Review outputs: Always review generated prompts before using them for indexing
  5. Iterate: Try different selection methods and parameters to find what works best

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Customizing generated prompts

After generation, you can manually edit the prompts in the output directory:
  1. Review the generated entity types and examples
  2. Add or remove entity types as needed
  3. Adjust the prompt instructions for your specific needs
  4. Test with a small index before running on full dataset

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Using tuned prompts

After generating prompts:
  1. Review the prompts in the prompts/ directory (or your custom output directory)
  2. Copy them to your project’s prompts/ directory if using a custom output location
  3. Run indexing - it will automatically use the new prompts: 
    graphrag index

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Performance considerations

  • Auto selection: Slower due to embedding generation but produces better quality
  • Sample size: Larger --n-subset-max values take longer but capture more diversity
  • Token limits: Higher --max-tokens allows for more examples but increases API costs

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Error handling

Common issues:
  • No input documents: Ensure documents exist in your input/ directory
  • API errors: Check that your API key is configured in .env
  • Insufficient samples: Increase --limit or --n-subset-max if you get too few examples
  • Language mismatch: Ensure --language matches your document language

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Next steps

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