You're building a system where a language model needs to give personalized guidance based on a user's profile, prior interactions, and stated goals. You need to decide when prompt design is enough and when task-specific fine-tuning is worth the added complexity.
How would you fine-tune or prompt-engineer a large language model for personalized guidance?
Prompt engineering for structured personalizationParameter-efficient fine-tuning for domain behaviorLLM evaluation for helpfulness and hallucination controlLanguage-model design trade-offs in a real product setting