Learn to evaluate project requirements like data privacy, latency, and task complexity to choose the most cost-effective model adaptation strategy.

A Decision Framework for Fine-Tuning vs. Prompt Engineering

Explore why Retrieval-Augmented Generation (RAG) and in-context learning often outperform fine-tuning for tasks requiring up-to-date information and source citations.

Improving Factual Accuracy with RAG and Few-Shot Prompting

Master Parameter-Efficient Fine-Tuning (PEFT) techniques to specialize models for specific tones and structured formats without massive hardware requirements.

Implementing Efficient Model Adaptation with LoRA and QLoRA

Analyze how fine-tuning smaller, open-source models can significantly reduce inference latency and API costs compared to prompting massive frontier models.

Scaling Specialized AI Workloads with Task-Specific Fine-Tuning

Navigate the critical trade-offs between modifying a model's internal weights through fine-tuning and steering its behavior using advanced prompt engineering and retrieval techniques.

Model Fine-Tuning & Prompting

A Decision Framework for Fine-Tuning vs. Prompt Engineering

Improving Factual Accuracy with RAG and Few-Shot Prompting

Implementing Efficient Model Adaptation with LoRA and QLoRA

Scaling Specialized AI Workloads with Task-Specific Fine-Tuning