1. Introduction
2. Team Structure3. Process Flow
3.1. Module Learning & Experience Design
3.2. Individual Flow Design
3.3. Narrative Design & Skills Mapping
3.4. New Assets Creation
3.5. Annex: World Building - Learning & Experience Design
3.6. Annex: World Building - Story Design
3.7. Process Flow Conclusion4. Caveats
4. Caveats5. LLM Prompt Engineering Techniques
5.1. Use The Latest Model
5.2. Zero-shot Prompting
5.3. Few-shot Prompting
5.4. Chain-Of-Thought Prompting
5.5. Structuring Prompts
5.6. Describing Prompts
5.7. Editing Prompts
5.8. Extending Responses
5.9. Multiple Users Collaborating6. Text-to-Image Prompting Engineering Techniques
6.1. General Techniques
6.2. Photography
6.3. Architecture
6.4. Various Aesthetic Styles
6.5. Product & Material5.2. Zero-shot Prompting
Zero-shot prompting is a concept in natural language processing (NLP) and artificial intelligence (AI), particularly in the context of language models like OpenAI's GPT series.
It refers to the ability of a model to understand and generate appropriate responses to a given input without having seen specific examples or training data for that particular task or prompt.
In other words, zero-shot prompting relies on the model's general understanding of language and knowledge acquired during its pre-training phase to generate meaningful responses to new prompts. This ability allows AI models to tackle a broad range of tasks without requiring fine-tuning or task-specific training, showcasing their versatility and adaptability in addressing various problems and situations.
Classify the text into neutral, negative, or positive.
Text: I think this learning module is okay.
Sentiment:
Neutral
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