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Explore the evolution of prompt engineering in generative AI and its vital role. Learn about managing prompts, evaluating models, and the future of prompt engineering. Dive into the nuances of language models, prompt update strategies, and the complexity of managing different models. Discover the adaptive strategies and future prospects of building tools based on first principles in a changing environment.
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Quick takeaways
- Prompt engineering is crucial for tuning inputs to language models, distinguishing it from traditional ML hyperparameter tuning.
- Tailored prompting strategies are necessary for interacting with different language models to elicit desired responses.
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Understanding Prompt Engineering and Its Evolution
Prompt engineering has emerged as a crucial skill alongside generative AI advancements, with a focus on tuning the inputs to language models (LMs). Starting from the GPT-3 days, the importance of prompt engineering became evident, leading to a deeper exploration of its potential. The term has evolved as more companies like Scale AI have embraced it, with a focus on tuning inputs, including prompts and model configurations, distinguishing it from traditional ML hyperparameter tuning.
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