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In production, log every prompt sent and every response received (with appropriate PII redaction). These logs are invaluable for debugging failures, detecting quality regressions, computing cost analytics, and building eval datasets from real traffic.

The datasets used to train frontier large language models are massive, often consisting of trillions of tokens. For example, the dataset for Llama 3 was over 15 trillion tokens, sourced from a vast collection of publicly available text and code from the internet.

Training state-of-the-art large language models requires massive datasets, significant computational power (often thousands of GPUs), and can cost millions of dollars.

LLMs are trained on vast datasets and can reflect societal biases. Critically evaluate outputs for fairness and consider techniques to mitigate bias in your applications.