Part 6/13:
- Post Fine-Tuning Improvements: The accuracy increased by roughly 14%, hallucinations (irrelevant or incorrect content) dropped from 26% to 4%, and response reliability scores slightly decreased but remained manageable.
One key insight is that prompt engineering alone can only take performance so far (~82-83%). Fine-tuning is essential to reach higher accuracy levels, especially for nuanced or procedural questions.
When Fine-Tuning Is Not Always the Best Choice
The speaker warns against premature fine-tuning without careful consideration:
Lack of clear use-case definition: Fine-tuning requires precise understanding of the task and data.
Resource and cost intensity: Data preparation and model retraining are expensive efforts.