> ## Documentation Index > Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt > Use this file to discover all available pages before exploring further. # Datasets & Fine-Tuning When building production AI applications, you need to improve model performance on specific tasks beyond what general-purpose models provide. Datasets & Fine-Tuning help you curate high-quality training data from your real production traffic and fine-tune models for better accuracy, consistency, and domain-specific performance. ## Why use Datasets & Fine-Tuning * **Production-ready datasets**: Transform your actual LLM requests into high-quality training data with scoring and filtering * **Seamless fine-tuning integration**: Export to JSONL or connect directly to fine-tuning platforms like OpenPipe * **Iterative improvement**: Use real performance data to continuously refine your datasets and models Helicone dataset curation interface with request filtering, scoring, and dataset management tools ## Quick Start Review your existing LLM requests in the Helicone dashboard and assign quality scores based on accuracy and relevance. You can score manually or use [automated scoring](/features/advanced-usage/scores) to identify your best examples. Score your requests Use Helicone's filtering system to find high-quality requests based on scores, dates, models, or custom properties. Filter your requests Choose the filtered requests you want to include and add them to a new or existing dataset. Review, organize, and refine your dataset by removing poor examples, balancing categories, and ensuring consistency. Export your curated dataset in JSONL format for use with fine-tuning platforms like OpenAI, Anthropic, or OpenPipe. ## Export Formats Helicone supports multiple export formats for different fine-tuning platforms: * **OpenAI JSONL**: Compatible with OpenAI's fine-tuning API * **Anthropic Format**: For Claude model fine-tuning * **Generic JSONL**: Works with most platforms * **CSV**: For data analysis and custom workflows ## Related Features Score LLM responses to identify high-quality training data Collect user feedback to improve dataset quality Version and manage prompts used in your training data Group related requests for better dataset organization