Model adaptation is a decision ladder. Start with clearer prompting and examples, add tools for actions, add retrieval for current or private knowledge, and fine-tune only when stable examples reveal a repeatable behavioral gap that cheaper interventions cannot fix. Post-training methods such as supervised fine-tuning, preference optimization, reinforcement learning, and low-rank adapters change model behavior but introduce data, evaluation, and operational costs.