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Post by gabriellejuliette33 on Nov 12, 2024 7:47:18 GMT
Advanced prompt engineering techniques involve using structured prompts, temperature control, and multi-prompt strategies. Structured prompts are highly specific, breaking down complex requests into smaller components to guide the AI more effectively. Temperature control, which adjusts the randomness of the AI's output, allows for more creative or deterministic results depending on the requirement. Another technique is multi-prompting, where multiple variations of a prompt are tested, and the best result is selected, ensuring more reliable outputs. Some engineers also employ chain-of-thought prompting, which guides the model through a series of logical steps to produce more coherent and reasoned outputs. These advanced strategies require deep knowledge of both the model’s capabilities and the intricacies of natural language. Click To Know More: www.blockchainappfactory.com/ai-prompt-engineering
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Post by vilgax048 on Nov 12, 2024 8:43:22 GMT
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Post by vilgax048 on Nov 12, 2024 8:48:07 GMT
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Post by pisowifi on Nov 19, 2024 14:57:29 GMT
I’ve been exploring prompt engineering and would love to dive into some advanced techniques. For example:
How do you effectively structure prompts for complex multi-step tasks? Are there ways to optimize for better contextual understanding in AI responses? Any tips for using few-shot or zero-shot learning approaches more efficiently?
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