Fine-Tuning

Fine-Tuning Techniques: LoRA, QLoRA, and Full Fine-Tuning Compared

Not all fine-tuning is created equal. Full fine-tuning updates every model weight and needs expensive hardware. LoRA injects small adapter matrices for 10x lower cost. QLoRA lets you fine-tune a 70B model on a single consumer GPU. The right choice depends on budget, dataset size, and target behavior.

Fine-Tuning Strategy: When to Use LoRA, Full Fine-Tuning, and RLHF

Not all fine-tuning is equal. The choice between LoRA, full fine-tuning, instruction tuning, and RLHF depends on your dataset size, target behavior, compute budget, and whether you need format compliance, domain accuracy, or value alignment. Choosing the wrong technique is expensive and often produces worse results.

What is Fine-Tuning in AI Training?

Fine-tuning is the process of teaching a pre-trained model new skills by training it further on task-specific examples. Think of it like hiring an experienced lawyer and training them on your company's specific legal style — you're not teaching them law from scratch, just adapting them to your context.

What is Fine-Tuning in AI Model Training?

Fine-tuning adapts a pre-trained AI model to a specific task or domain by continuing its training on a targeted, smaller dataset. Instead of training from scratch — which requires massive compute — fine-tuning transfers general capabilities and specializes them, often achieving expert-level performance with thousands, not billions, of examples.

When Fine-Tuning Beats Prompting: Concrete Decision Criteria

Prompting is cheaper, faster to iterate, and preserves model flexibility. Fine-tuning gives better consistency, lower inference cost, and tighter style control. Knowing exactly when to reach for fine-tuning versus sticking with clever prompts saves teams from wasted training budgets on problems that didn't need solving that way.

Fine-Tuning vs. RAG: Which Should You Use?

Both let you customize AI for your use case. But they work completely differently, cost different amounts, and solve different problems. Here's how to choose.