Prompt Engineering & Fine-Tuning — Explained Simply
About this lesson
Same model, wildly different results — it all comes down to how you use it. This is the practical finale: how to get great answers by prompting well, and how to teach a model your own data with fine-tuning. In Episode 3 of this chapter on Transformers & Large Language Models, we cover the two levers that matter most in everyday use. First, prompt engineering: the building blocks of a good prompt, being specific, few-shot examples, chain-of-thought, and controlling the output. Then fine-tuning: how it works, how it compares to RAG, and the affordable LoRA approach that runs on modest hardware. We finish with simple, copy-paste code for both. What you'll learn: • The five building blocks of a strong prompt • Being specific, few-shot examples, and chain-of-thought • How to control tone, format, and length • Common prompt mistakes — and the fixes • How fine-tuning actually works • Prompting vs RAG vs fine-tuning — when to use each • LoRA: affordable fine-tuning on modest hardware New here? Start with Episode 1 (“The Attention Mechanism” - https://youtu.be/WoVrmHx2fy0) and Episode 2 (“BERT, GPT & Vision Transformers” - https://youtu.be/s2XMYb9pN5U) — this episode builds on both. Thank you for watching! #PromptEngineering #FineTuning #LLM
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