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Lessons from building a small-scale AI application
7 Lessons from building a small-scale AI application for a year
Beyond prompts and fine-tuning, adjusting hyperparameters (e.g., learning rates, batch sizes, and optimizer settings) can improve the model’s training efficiency and performance. While traditional RPC resilience patterns can mitigate transient network issues or minor delays, they were insufficient to address the inherent latency, variability, and processing overhead introduced by LLMs. The task queue also provided built-in resilience for retries, traffic smoothing, and dynamic scaling of worker pools.
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