ai-llm-development

Fine-tuning, prompting, and RAG serve different needs: prompting works for general tasks with few examples, fine-tuning adapts models to specific domains using instruction datasets, and RAG retrieves external knowledge without retraining. Choose based on your data availability, latency requirements, and whether you need domain-specific behavior or real-time information.

Instruction tuning datasets pair user queries with desired model responses. Structure your data around real use cases, ensure diverse examples covering edge cases, and format consistently for your framework. Quality and relevance matter more than raw size; validate that examples reflect actual production scenarios.

LoRA and PEFT reduce memory and compute costs by updating only adapter layers instead of full model weights. Load your instruction dataset, configure rank and alpha parameters, train on the adapter, and merge weights into the base model. This approach achieves comparable performance to full fine-tuning with a fraction of resources.

Yes, use LLM-as-judge evaluation to score model outputs programmatically and run A/B testing to compare variants. Define clear evaluation criteria, use reference models or scoring rubrics, and track metrics over time. This provides continuous feedback for iteration without requiring exhaustive human review.

Use PEFT when you have limited computational resources, need to maintain base model performance, or want to deploy multiple specialized adapters efficiently. PEFT is ideal for fine-tuning large models on consumer hardware or when you need rapid adaptation across multiple tasks.

Build evaluation combining automated metrics (LLM-as-judge, semantic similarity), human-in-the-loop validation, and A/B testing. Track performance across diverse scenarios, monitor drift over time, and integrate guardrails to catch unsafe outputs. Continuous evaluation ensures quality as your model and data evolve.