This case study explores the challenges and solutions implemented by the developers of JOBifAI, a game that integrates LLMs to create innovative gameplay mechanics. The study provides valuable insights into the practical challenges of deploying LLMs in production, particularly focusing on the implementation and impact of safety filters in an interactive entertainment context. ## Overview and Context JOBifAI is an interactive game where players engage with AI-generated content in a job interview scenario. The game's premise involves players submitting an AI-generated portfolio and participating in subsequent interactions. This setup creates an environment where maintaining appropriate social behavior is integral to the gameplay mechanics. ## Technical Implementation and Challenges The development team implemented a sophisticated prompt structure for handling player interactions: * Each prompt includes three main components: * Context * Player action * Potential action categories The system expects responses in a specific JSON format: `{"choice": c, "sentence": s}`, where 'c' represents the category of the player's action, and 's' provides the resulting description. The team identified three primary failure modes in their production environment: * JSON parsing failures (HTTP 400 responses) * Schema validation failures (even after type casting attempts) * Safety filter rejections (also resulting in HTTP 400 responses) A significant challenge in production was the inability to differentiate between technical failures and safety-related rejections, as they all resulted in the same HTTP status code. This lack of granularity in error handling forced the team to implement a somewhat crude but effective retry mechanism. ## Production Solution and Performance The development team implemented a three-retry system to handle failures: * First attempt: ~75% success rate * Second attempt: ~90% cumulative success rate * Third attempt: ~99% cumulative success rate These success rates, while not based on hard metrics, were derived from extensive playtesting and real-world usage patterns. The solution, while not elegant, proved effective enough for the proof-of-concept stage. ## Safety Filter Implementation Analysis The case study provides valuable insights into the practical implications of safety filters in production LLM applications: * Inconsistent Behavior: The safety filters often triggered unpredictably, making it difficult to implement reliable error handling * Cost Implications: Multiple retries increased the cost per query, particularly for users whose inputs frequently triggered safety filters * User Experience Impact: The lack of clear error differentiation made it challenging to provide appropriate feedback to users The team identified several potential improvements for safety filter implementations in production environments: * Granular Error Codes: Suggesting the implementation of specific error codes for different types of safety concerns: * Sensitive topic warnings * Personal information verification needs * Complete rejection for unsafe content * Cost Management: The current implementation forces developers to either absorb or pass on the costs of multiple retries to users, creating uncertainty in the business model ## Production Limitations and Impact The case study reveals several critical limitations of current safety filter implementations in production: * Reliability Issues: Safety filters proved to be even less reliable than standard LLM responses * Resource Waste: The need for multiple retries led to unnecessary computation and increased costs * Development Constraints: The unreliable foundation of safety filters ultimately deterred the team from expanding beyond the proof-of-concept stage ## Lessons Learned and Recommendations The case study offers several valuable insights for LLMOps practitioners: * Error Handling Design: Implement robust retry mechanisms while being mindful of cost implications * Safety Filter Integration: Consider the balance between safety requirements and user experience * Cost Management: Plan for and monitor the impact of retry mechanisms on operational costs * Error Transparency: Push for more granular error reporting from LLM providers The experience of JOBifAI demonstrates that while safety filters are necessary, their current implementation creates significant challenges for production applications. The case study suggests that more transparent and reliable safety filter implementations would enable developers to build better user experiences while maintaining appropriate safety standards. ## Future Considerations The case study points to several areas for improvement in LLMOps practices: * Better error handling and reporting mechanisms from LLM providers * More transparent safety filter implementations * Cost-effective retry strategies * Clear differentiation between technical and content-based failures While JOBifAI successfully implemented a working solution, the case study highlights the need for more sophisticated approaches to safety filters in production LLM applications, particularly for interactive and real-time use cases.