Prompt engineering is a key technique when working with AI models like GPT in generating accurate and useful outputs. Here are some of the top techniques used today:
- Few-Shot Learning: Provide a few examples to help the model mimic a task format.
- Zero-Shot Learning: Ask the model to perform a task without examples, relying on its pre-existing knowledge.
- Chain-of-Thought Prompting: Encourage the model to reason step-by-step for complex tasks.
- Instruction-Based Prompting: Give explicit instructions to ensure clear outputs.
- Role-Based Prompting: Assign the AI a specific role, influencing tone or expertise.
- Contextual Prompting: Provide background information to improve accuracy.
- Iterative Prompting: Refine prompts based on previous responses for better results.
- Prompt Templates: Reuse structured prompts for similar tasks.
- Multimodal Prompting: Combine text with other media for more versatile interactions.
- Dynamic Prompting: Adapt prompts to user needs in real-time.
- Temperature and Sampling Control: Adjust randomness for creative or focused outputs.
- Prompt Chaining: Break down complex tasks into smaller steps.
These techniques enhance AI performance across diverse applications.
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5 个月That’s an incredibly useful list! Prompt engineering a, if not the, crucial future skills in all knowledge worker professions. Including mine (legal advisors). We’re all good advised by leveling up our prompt engineering game: https://powerclaim.io/courses/prompt-engineering/