Quick takeaways

• Customizing large language models through prompt engineering and fine-tuning offers flexibility in tailoring responses to specific tasks and domains.

• Human annotators play a crucial role in evaluating the performance of large language models, relying on their generalization capacity and the subjective nature of language understanding.

• Successful deployment of large language models requires optimizing infrastructure, considering factors like latency, quantization, and specialized hardware, to achieve a balance between accuracy and speed.

Deep dives