What is prompt engineering?

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Prompt engineering is a critical practice in optimizing the outputs of generative artificial intelligence (AI) systems. By refining prompts, engineers enhance AI’s ability to comprehend and respond accurately to various queries, thus reducing biases and confusion. Prompt engineering involves crafting high-quality inputs that help AI models understand nuances and intent, significantly improving the relevance and quality of generated content such as text, code, or images. By ibm.com.

The main pints in the article:

  • How does prompt engineering work?
  • What are the benefits of prompt engineering?
  • What skills does a prompt engineer need?
    • Zero-shot prompting
    • Few-shot prompting or in-context learning
    • Chain-of-thought prompting (CoT)

Generative AI operates on transformer architectures and natural language processing (NLP), utilizing large datasets and machine learning algorithms to produce meaningful outputs from textual inputs. Techniques like tokenization, model parameter tuning, and top-k sampling are integral to effective prompt engineering. These methods enable foundation models, such as OpenAI’s GPT series or Google Bard, to deliver optimized results with minimal post-generation editing. Good read!

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