Prompt engineering involves optimizing prompts to guide Large Language Models (LLMs) using techniques like few-shot learning and in-context learning, enabling effective steering of LLMs for various NLP tasks.
The process of prompt engineering includes tokenization, encoding into numerical representations, feeding into LLM's architecture for pattern recognition, and decoding output tokens into human-readable text.
Coding implementation of prompt engineering can be demonstrated using the GPT-2 model and Transformers library in Python, showcasing how to generate responses based on prompts.
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