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PROMPT ENGG
What is prompt engineering?
Prompt engineering is a powerful tool to help AI chatbots generate contextually relevant and coherent responses in real-time conversations.
Prompt engineering helps generative AI models better comprehend and respond to a wide range of queries, from the simple to the highly technical. Generative artificial intelligence (AI) systems are designed to generate specific outputs based on the quality of provided prompts. Generative AI relies on the iterative refinement of different prompt engineering techniques to effectively learn from diverse input data and adapt to minimize biases, confusion and produce more accurate responses.
How does prompt engineering work?
Generative AI models are built on transformer architectures, which enable them to grasp the intricacies of language and process vast amounts of data through neural networks.AI models generate helpful responses, including tokenization, model parameter tuning and top-k sampling.
Generative AI models operate based on natural language processing (NLP) and use natural language inputs to produce complex results. The underlying data science preparations, transformer architectures and machine learning algorithms enable these models to understand language and then use massive datasets to create text or image outputs.
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Text-to-image generative AI like DALL-E and Midjourney uses an LLM in concert with stable diffusion, a model that excels at generating images from text descriptions. Effective prompt engineering combines technical knowledge with a deep understanding of natural language, vocabulary and context to produce optimal outputs with few revisions.
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What are the benefits of prompt engineering?
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The primary benefit of prompt engineering is the ability to achieve optimized outputs with minimal post-generation effort. Generative AI outputs can be mixed in quality, often requiring skilled practitioners to review and revise. By crafting precise prompts, prompt engineers ensure that AI-generated output aligns with the desired goals and criteria, reducing the need for extensive post-processing.
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