This hands-on session explains when fine-tuning is the right choice (vs. prompts/RAG), what data you need, and how to prep it for reliable results. We’ll walk through a minimal, end-to-end demo: cleaning and tokenizing a small instruction dataset, applying PEFT/QLoRA for a lightweight fine-tune, and comparing before/after performance. You’ll learn How to decide if/when to fine-tune based on task, data volatility, and ROI Practical preprocessing: de-duplication, normalization, tokenization, and split strategy A live notebook demo to run locally or in Colab, with quick evaluation metrics Note: This Masterclass is part of the AI Residency. New cohort starts Saturday, 23 Aug (GST). Join now. https://academy.decodingdatascience.com/airesidencyfasttrack