AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Fine-tuning and when it is justified
Fine-tuning trains an existing model further using task-specific examples. It changes learned behavior rather than simply adding a document to the current prompt. It can target formatting, domain task performance, style, or consistent instruction following.
LoRA, QLoRA, and PEFT
PEFT means parameter-efficient fine-tuning: update a smaller subset of parameters instead of the whole model. LoRA trains small low-rank adapter matrices attached to selected model operations. QLoRA combines adapter training with a quantized frozen base model…
Dataset preparation, evaluation, and RAG comparison
Dataset preparation converts representative examples into the selected training format with reviewed expected outputs. Evaluation compares trained behavior against the baseline on separate data. RAG supplies current external knowledge at runtime; fine-tuning…
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