Part 8/13:
Explore alternative methods: Use retrieval techniques like RAG and traditional embedding-based algorithms.
Generate synthetic data: When labeled data is scarce, create high-quality synthetic datasets to augment training.
Evaluate cost-benefit trade-offs: Determine if the improved accuracy justifies the investment in fine-tuning.
He emphasizes that data quality is often more crucial than quantity. Polluted or poorly labeled data can degrade performance and may do more harm than good.
Defining Success and Readiness
Ensuring a project is ready for fine-tuning involves:
Clear problem definition
Benchmarking initial results
Validating with multiple techniques