Using ChatGPT for retry logic in high-load systems presents significant challenges, primarily due to its inherent latency and per-token cost model, making it economically and performance-wise impractical for frequent, real-time decisions. Furthermore, the non-deterministic nature of large language models means retry strategies might vary for identical inputs, leading to unpredictable system behavior and making auditing and debugging complex. Relying on an external API introduces a single point of failure and subjects the retry mechanism to external service availability and rate limits, which can easily be exhausted in high-throughput environments. Managing the necessary context about past failures and system state within prompts adds further complexity and prompt engineering overhead. Consequently, such an approach is generally ill-suited for critical, performance-sensitive retry logic, where predictability and low overhead are paramount. More details: https://svb.trackerrr.com/pingback.php?url=https://abcname.com.ua/