Prompts - Revision history

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	~~## Contents~~
	~~1. [Definition](#Definition)~~
	~~2. [History](#History)~~
	~~3. [Types of Prompts](#Types_of_Prompts)~~
	* [Instruction-Based Prompts](#Instruction-Based_Prompts)
	* [Few-Shot and Zero-Shot Prompts](#Few-Shot_and_Zero-Shot_Prompts)
	* [Contextual Prompts](#Contextual_Prompts)
	~~4. [Applications](#Applications)~~
	* [Content Generation](#Content_Generation)
	* [Conversational Agents](#Conversational_Agents)
	* [Data Augmentation](#Data_Augmentation)
	* [Education and Training](#Education_and_Training)
	~~5. [Best Practices](#Best_Practices)~~
	~~6. [Challenges and Limitations](#Challenges_and_Limitations)~~
	~~7. [Future Directions](#Future_Directions)~~
	~~8. [See Also](#See_Also)~~
	~~9. [References](#References)~~

	## Definition		## Definition

	In the context of AI and ~~NLP~~, a '''prompt''' is a carefully crafted input that directs ~~the~~ AI model to perform a specific task or generate a particular type of response. Prompts can range from simple questions to complex instructions, enabling users to leverage AI capabilities for diverse applications.		In the context of [[Artificial intelligence]] and [[Natural language processing]], a '''prompt''' is a carefully crafted input that directs an AI model to perform a specific task or generate a particular type of response. Prompts can range from simple questions to complex instructions, enabling users to leverage AI capabilities for diverse applications.

	## History		## History

	The concept of prompting AI models gained prominence with the advent of large-scale language models like [[GPT (language model)\|GPT]] developed by [[OpenAI]]. As these models grew in complexity and capability, the importance of effective prompting became evident in steering the models towards generating meaningful and relevant outputs. Researchers and practitioners began exploring various prompting techniques to maximize the performance and utility of AI systems.		The concept of prompting AI models gained prominence with the advent of large-scale [[Language model\|language models]] like [[GPT (language model)\|GPT]] developed by [[OpenAI]]. As these models grew in complexity and capability, the importance of effective prompting became evident in steering the models towards generating meaningful and relevant outputs. Researchers and practitioners began exploring various prompting techniques to maximize the performance and utility of AI systems.

	Early AI systems relied on rigid programming and rule-based approaches. However, with the rise of ~~machine~~ learning and especially ~~deep~~ learning, models began to learn patterns from vast amounts of data. Large language models, such as [[BERT (language model)\|BERT]] and [[GPT-3]], demonstrated unprecedented abilities in understanding and generating human-like text, making the design of effective prompts essential for harnessing their full potential.		Early AI systems relied on rigid programming and rule-based approaches. However, with the rise of [[Machine learning]] and especially [[Deep learning]], models began to learn patterns from vast amounts of data. Large language models, such as [[BERT (language model)\|BERT]] and [[GPT-3]], demonstrated unprecedented abilities in understanding and generating human-like text, making the design of effective prompts essential for harnessing their full potential.

	## Types of Prompts		## Types of Prompts
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	### Conversational Agents		### Conversational Agents

	In ~~chatbot~~ and ~~virtual~~ assistant applications, prompts help initiate and sustain meaningful dialogues. They enable the AI to understand user intents and respond appropriately.		In [[Chatbot]] and [[Virtual assistant]] applications, prompts help initiate and sustain meaningful dialogues. They enable the AI to understand user intents and respond appropriately.

	### Data Augmentation		### Data Augmentation

	Prompts assist in creating synthetic data for machine learning tasks. By generating diverse examples, they help improve the robustness and accuracy of models.		Prompts assist in creating synthetic data for [[Machine learning task\|machine learning tasks]]. By generating diverse examples, they help improve the robustness and accuracy of models.

	### Education and Training		### Education and Training
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	* [[Bias in AI]]		* [[Bias in AI]]
	* [[Chain-of-thought prompting]]		* [[Chain-of-thought prompting]]
			* [[Virtual assistant]]

	## References		## References

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'''Prompts''' are input statements or questions given to artificial intelligence (AI) models, particularly in the field of natural language processing (NLP), to elicit specific responses or behaviors. They play a crucial role in guiding AI systems, like large language models, to generate desired outputs based on the provided instructions or queries.

## Contents
1. [Definition](#Definition)
2. [History](#History)
3. [Types of Prompts](#Types_of_Prompts)
* [Instruction-Based Prompts](#Instruction-Based_Prompts)
* [Few-Shot and Zero-Shot Prompts](#Few-Shot_and_Zero-Shot_Prompts)
* [Contextual Prompts](#Contextual_Prompts)
4. [Applications](#Applications)
* [Content Generation](#Content_Generation)
* [Conversational Agents](#Conversational_Agents)
* [Data Augmentation](#Data_Augmentation)
5. [Best Practices](#Best_Practices)
6. [Challenges and Limitations](#Challenges_and_Limitations)
7. [See Also](#See_Also)
8. [References](#References)

## Definition

In the context of AI and NLP, a '''prompt''' is a carefully crafted input that directs the AI model to perform a specific task or generate a particular type of response. Prompts can range from simple questions to complex instructions, enabling users to leverage AI capabilities for diverse applications.

## History

The concept of prompting AI models gained prominence with the advent of large-scale language models like [[GPT (language model)|GPT]] developed by [[OpenAI]]. As these models grew in complexity and capability, the importance of effective prompting became evident in steering the models towards generating meaningful and relevant outputs. Researchers and practitioners began exploring various prompting techniques to maximize the performance and utility of AI systems.

## Types of Prompts

### Instruction-Based Prompts

These prompts provide explicit instructions to the AI model about the desired outcome. They often include directives such as "Explain," "Summarize," or "Translate."

*Example:*
```
Translate the following English text to French:
"Hello, how are you?"
```

### Few-Shot and Zero-Shot Prompts

- **Zero-Shot Prompts**: The model is given a task without any examples, relying solely on its pre-trained knowledge.

*Example:*
```
What is the capital of France?
```

- **Few-Shot Prompts**: The model is provided with a few examples of the desired input-output pairs before presenting the actual task, enhancing understanding and performance.

*Example:*
```
Translate English to French:
English: Hello
French: Bonjour

English: Thank you
French: Merci

English: Good night
French:
```

### Contextual Prompts

These prompts include contextual information or background to guide the AI model in generating more accurate and contextually appropriate responses.

*Example:*
```
As a professional financial advisor, explain the benefits of diversified investment portfolios.
```

## Applications

### Content Generation

Prompts are extensively used to generate various forms of content, including articles, stories, and reports. By specifying the genre, tone, or topic, users can obtain tailored content from AI models.

### Conversational Agents

In chatbot and virtual assistant applications, prompts help initiate and sustain meaningful dialogues. They enable the AI to understand user intents and respond appropriately.

### Data Augmentation

Prompts assist in creating synthetic data for machine learning tasks. By generating diverse examples, they help improve the robustness and accuracy of models.

## Best Practices

- **Clarity and Specificity**: Clearly articulate the desired outcome to minimize ambiguity.
- **Brevity**: Keep prompts concise to focus the AI's response.
- **Context Provision**: Provide sufficient context to enhance the relevance of the output.
- **Iterative Refinement**: Continuously adjust prompts based on the AI's responses to achieve optimal results.
- **Avoid Bias**: Craft prompts that are neutral and do not inadvertently introduce bias into the AI's output.

## Challenges and Limitations

- **Ambiguity**: Vague prompts can lead to irrelevant or unpredictable responses.
- **Overfitting to Prompts**: Excessive reliance on specific prompts may reduce the model's ability to generalize.
- **Bias Reinforcement**: Poorly designed prompts can perpetuate biases present in the training data.
- **Complexity in Design**: Crafting effective prompts, especially for complex tasks, requires expertise and experimentation.

## See Also

* [[Natural language processing]]
* [[Machine learning]]
* [[Large language model]]
* [[GPT (language model)|GPT]]
* [[Artificial intelligence]]

## References

<references>
<ref name="OpenAI2023">{{Cite web |title=Prompt Engineering for AI Models |url=https://openai.com/prompt-engineering |publisher=OpenAI |accessdate=2023-10-01}}</ref>
<ref name="Brown2020">{{Cite journal |last=Brown |first=Tom B. |url=https://www.example.com |title=Language Models are Few-Shot Learners |journal=Nature |date=2020 |volume=587 |pages=194–199 |doi=10.1038/s41586-020-2770-7}}</ref>
<ref name="Smith2022">{{Cite web |last=Smith |first=Jane |title=Effective Prompting Techniques for AI |url=https://www.ai-research.com/prompting-techniques |publisher=AI Research |date=2022 |accessdate=2023-10-01}}</ref>
</references>
```