Prompt engineering has become a powerful method for optimizing language models in natural language processing (NLP). It entails creating efficient prompts, often referred to as instructions or questions, to direct the behavior and output of AI models.
Due to prompt engineering’s capacity to enhance the functionality and management of language models, it has attracted a lot of attention. This article will delve into the concept of prompt engineering, its significance and how it works.
Pre-transformer era (Before 2017)
Pre-training and the emergence of transformers (2017)
Fine-tuning and the rise of GPT (2018)
Advancements in prompt engineering techniques (2018–present)
Community contributions and exploration (2018–present)
Ongoing research and future directions (present and beyond)
Improved control
Reducing bias in AI systems
Modifying model behavior
Specify the task
Identify the inputs and outputs
Create informative prompts
Iterate and evaluate
Calibration and fine-tuning
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