fine-tuning
3 videos across 3 channels
Fine-tuning tailors a model by updating its weights on specialized data to produce domain-specific outputs, contrasting with prompt design and retrieval-augmented approaches. Through vivid examples—from workplace policy and HR queries to music-model experiments and personal benchmarks—learn how data quality, compute, and evaluation shape results, and how practitioners decide when fine-tuning is worth the investment vs. other customization techniques.
RAG vs Fine Tuning vs Prompt Engineering: Use Cases And Key Differences Explained | Simplilearn
The video introduces three core AI customization techniques—prompt engineering, retrieval-augmented generation (RAG), an
00:25:32
I Trained My Own AI... It beat ChatGPT
The video chronicles the creator's chaotic journey training and benchmarking an AI model, detailing data collection, for
00:25:35