Building LLM Fine-Tuning Datasets From Production Logs

This article discusses the critical process of building effective fine-tuning datasets from production logs for large language models. It emphasizes that raw logs are not datasets and highlights the importance of selecting high-signal examples, such as user edits, validator failures, or requests routed to more expensive models. The author also advises on data cleaning techniques, recommending redaction with surrogates, deduplication using MinHash, capping per-source contributions, and filtering truncated outputs. Finally, it warns against temporal and near-duplicate leakage across training and testing splits, suggesting deduplication before splitting and using time-based splits to ensure accurate evaluation. AI

IMPACT Provides a methodology for improving LLM performance through curated fine-tuning datasets derived from real-world usage.

RANK_REASON Article describes a method for creating training data for AI models, which is a tool or technique.

Read on dev.to — LLM tag →

• dataset
• Jaccard
• JSON
• MinHash
• production logs
• validator

• product
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →