When building AI applications that need domain-specific knowledge or consistent formatting, generic models often fall short. Fine-tuning lets you train models on your curated datasets from Helicone, creating specialized AI that understands your unique requirements and consistently delivers the exact outputs your application needs.

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Why use Fine-tuning

  • Create domain experts: Train models on your specific data to excel at specialized tasks like legal analysis or medical coding
  • Ensure consistent outputs: Get reliable formatting and structure that matches your exact specifications every time
  • Reduce costs and latency: Smaller fine-tuned models often outperform larger generic models at specific tasks
Dataset curation interface showing request filtering and evaluation for fine-tuning

Helicone's dataset curation interface for preparing fine-tuning data

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Quick Start

1

Curate your dataset

Filter and select high-quality request-response pairs from your Helicone logs:
// Navigate to Datasets in Helicone dashboard
// Apply filters to find ideal training examples:
// - Status: 200 (successful requests only)
// - Model: gpt-4o-mini (or your preferred base model)
// - Custom properties for specific use cases
2

Export for fine-tuning

Export your curated dataset in the format your fine-tuning platform requires:
# Export as JSONL for OpenAI
helicone datasets export --format openai --id dataset-123

# Export for other platforms
helicone datasets export --format anthropic --id dataset-123
helicone datasets export --format cohere --id dataset-123
3

Create fine-tuning job

Use your exported dataset with your chosen platform:
# OpenAI fine-tuning example
import openai

# Upload training file
file = openai.File.create(
  file=open("helicone-dataset.jsonl", "rb"),
  purpose='fine-tune'
)

# Create fine-tuning job
job = openai.FineTuningJob.create(
  training_file=file.id,
  model="gpt-4o-mini"
)

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Configuration Options

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Basic Settings

Core options for dataset curation and export:
SettingTypeDescriptionDefaultExample
minScorenumberMinimum score for including requests00.8
maxTokensnumberMaximum tokens per example∞4096
formatstringExport format for platform"openai""anthropic"
includeMetadatabooleanInclude Helicone metadatafalsetrue

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Advanced Settings

SettingTypeDescriptionDefaultExample
validationSplitnumberPercentage for validation set0.20.15
deduplicationbooleanRemove duplicate examplestruefalse
samplingobjectSampling configuration{}{"method": "random", "size": 1000}

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Detailed Explanations

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Use Cases

Fine-tune a model on your best support conversations for consistent, brand-aligned responses:
// 1. Tag high-quality support conversations
const response = await openai.chat.completions.create(
  {
    model: "gpt-4o-mini",
    messages: supportConversation
  },
  {
    headers: {
      "Helicone-Property-Quality": "excellent",
      "Helicone-Property-UseCase": "support",
      "Helicone-Property-Resolved": "true"
    }
  }
);

// 2. Create dataset from tagged conversations
const dataset = await helicone.datasets.create({
  name: "support-agent-training",
  filters: {
    properties: {
      Quality: "excellent",
      UseCase: "support",
      Resolved: "true"
    },
    minScore: 0.9
  }
});

// 3. Fine-tune on curated examples
const fineTunedModel = await trainModel(dataset);

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Understanding Fine-tuning

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When to Use Fine-tuning vs RAG

Fine-tuning and RAG (Retrieval Augmented Generation) solve different problems: What Fine-tuning is best for:
  • Teaching consistent behavior and output formats
  • Improving performance on specific tasks
  • Reducing latency and costs with smaller models
  • Encoding domain knowledge into the model
What RAG is best for:
  • Working with frequently changing information
  • Handling large knowledge bases
  • Maintaining source attribution
  • Quick updates without retraining
// ✅ Good fine-tuning use case
// Teaching a model to always format API responses consistently
const apiResponseExample = {
  input: "Get user data for ID 123",
  output: {
    status: "success",
    data: { id: 123, name: "John" },
    metadata: { timestamp: "2024-01-01T00:00:00Z" }
  }
};

// ❌ Poor fine-tuning use case  
// Trying to teach current product inventory (use RAG instead)
const inventoryExample = {
  input: "What's in stock?",
  output: "SKU-123: 45 units, SKU-456: 0 units" // This changes daily!
};

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Dataset Quality Guidelines

The quality of your fine-tuning dataset determines model performance: Key principles:
  • Diversity: Include varied examples covering edge cases
  • Consistency: Ensure similar inputs have similar outputs
  • Quality: Only include high-quality, verified examples
  • Quantity: Start with 50-100 examples minimum
Quality indicators in Helicone: 
// High-quality dataset criteria
const qualityDataset = {
  filters: {
    // Performance indicators
    responseTime: { max: 3000 },
    tokenCount: { min: 50, max: 2000 },
    
    // Quality signals
    scores: { 
      userFeedback: { min: 4 },
      accuracy: { min: 0.9 }
    },
    
    // Consistency checks
    status: "success",
    errorRate: { max: 0.01 }
  }
};

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Fine-tuning Workflow Best Practices

Data preparation checklist:
  • Review examples for quality and consistency
  • Remove PII and sensitive information
  • Balance dataset across different use cases
  • Validate format matches platform requirements
Monitoring fine-tuned models: 
// Track fine-tuned model performance
const response = await openai.chat.completions.create(
  {
    model: "ft:gpt-4o-mini:org-name:model-name:id",
    messages: [{ role: "user", content: prompt }]
  },
  {
    headers: {
      "Helicone-Property-Model-Type": "fine-tuned",
      "Helicone-Property-Base-Model": "gpt-4o-mini",
      "Helicone-Property-Training-Job": "ftjob-123"
    }
  }
);

// Compare against base model performance

Datasets

Create and manage training datasets from your Helicone requests

Scores

Score requests to identify high-quality training examples

Request Filtering

Advanced filtering to find the perfect training examples

Custom Properties

Tag requests for easier dataset curation