Structured Exploration vs. Generative Flexibility: A Field Study Comparing Bandit and LLM Architectures for Personalised Health Behaviour Interventions

A 4-week study comparing bandit algorithms and LLM architectures for personalized health behavior interventions found that LLM-based messaging approaches were rated more helpful than templates, but contextual bandit optimization provided no additional benefit over LLM-only methods. The research reveals a trade-off between structured exploration of behavior change techniques and generative flexibility in AI health systems.