Transitioning a language model from prototype to production can be enhanced through fine-tuning on application-specific data, a process streamlined by services like OpenAI and HuggingFace. Fine-tuning involves selecting high-quality data from the unique application context, which can be facilitated by tools such as LangSmith and Lilac. LangSmith efficiently collects and manages datasets generated by LLM applications, capturing quality examples and feedback, while Lilac offers advanced analytics to refine these datasets. These tools complement each other by allowing users to curate datasets that optimize language model performance through processes like signal computation for near-duplicates and PII detection, concept organization, and custom labeling. The curated datasets can be exported and used to fine-tune models, which can then be integrated back into applications to improve their contextual reasoning capabilities. This comprehensive approach ensures more consistent and high-quality behavior in language model applications.