Fine-tuning playbook prioritizes evaluation and data quality over training

This guide outlines a generic fine-tuning playbook, emphasizing that the most critical steps occur before and after the training process, not during. It advises trying less expensive interventions like better prompting or retrieval-augmented generation (RAG) before resorting to fine-tuning. The playbook stresses building a held-out evaluation set before collecting training data to establish a baseline and ensure the evaluation itself is sound, followed by careful data curation and deduplication to avoid overfitting. AI

IMPACT Provides a structured approach to fine-tuning LLMs, potentially improving efficiency and effectiveness.

RANK_REASON The item describes a methodology for fine-tuning models, which is a research-oriented topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• LoRA+
• retrieval-augmented generation

• model release

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