Create expert identity for task

Input your task description to create optimized prompt with expert identity. Additionally, you can create samples for input/output and reasoning steps.

Input/Output Samples

Generate samples of input and output for your task. It can help with guiding model responses using in-context learning.

COT Generation

Generate Chain-of-Thought reasoning for task. It can help improve model reasoning capabilities.

About AI Prompt Generator

The Prompt Generator is a tool designed to create prompts for various tasks. It uses popular approaches to the prompt generation to ensure the produced prompts are efficient and high-performing.

Getting Started with Prompt Generation

To start using prompt generator, provide task description with desired behavior. Specify information like format and other important details.

How to Use AI Prompt Generator

Using the AI Prompt Generator is straightforward and efficient. Start by providing a detailed description of the task you are tackling. It is important to include all the essential information, such as the specific format you need or any particular constraints you have in mind.
Clearly outline what you are inputting into the generator and what you expect as the output. This might include specific themes, keywords, styles, or any particular requirements unique to your project. The more detailed your description, the more tailored and effective the generated prompts will be.
The AI Prompt Generator also offers advanced features for enhanced creativity and precision. For instance, you can use the Chain of Thought generation to create more complex and layered prompts. This feature helps in generating prompts that follow a logical progression, making them ideal for detailed and intricate projects.
Additionally, enable the generation of input-output samples. This feature is particularly useful for in-context learning, allowing you to see examples of how your inputs transform into outputs. It is a great way to refine your prompts and ensure they align perfectly with your project goals.

Based on papers

ExpertPrompting

Main Paper Idea
Expert Prompting is a method to improve the response quality of large language models (LLMs) by creating detailed, customized expert identities for each instruction. This approach allows LLMs to respond as if they are experts in specific fields.
ExpertPrompting paper image illustration
Why it works
ExpertPrompting works effectively because it leverages the concept of In-Context Learning to automatically generate detailed expert identities tailored to each specific instruction. This leads to more informed and precise responses from LLMs. By responding from the perspective of a knowledgeable expert, the LLMs provide more comprehensive and accurate answers.

Example 1: Atomic Structure Explanation

When asked to describe the structure of an atom, the LLM, under ExpertPrompting, assumes the identity of a physicist specialized in atomic structure. This results in a detailed explanation of the atom's components, including the nucleus made of protons and neutrons, and the electrons orbiting in shells. This response is more precise and in-depth compared to a standard LLM response, showcasing the effectiveness of the ExpertPrompting method in enhancing the quality of technical answers.

Example 2: Environmental Science Inquiry

In responding to a query about the effects of deforestation, the LLM, through ExpertPrompting, takes on the identity of an environmental scientist. It provides a comprehensive list of deforestation effects, including biodiversity loss, climate change impact, and soil erosion. This demonstrates how ExpertPrompting can be applied to environmental science prompts, enabling the LLM to offer detailed, expert-level insights into complex ecological issues.

Chain-of-Thought Prompting

Main Paper Idea
The paper "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" explores a method called "Chain-of-Thought Prompting" to improve complex reasoning abilities in large language models (LLMs). The method involves generating a series of intermediate reasoning steps, which significantly enhances the model's performance on tasks requiring complex arithmetic, commonsense, and symbolic reasoning. The approach shows notable empirical gains, especially with models like PaLM 540B, outperforming even fine-tuned GPT-3 models on benchmarks like GSM8K.
Chain-of-Thought Prompting paper image illustration
Why it works
Chain-of-Thought Prompting is effective because it breaks down complex problems into intermediate steps, enabling more computational resources to be allocated for reasoning. It provides an interpretable window into the model's thought process and is applicable to a wide range of tasks. Importantly, this method can be elicited in large, off-the-shelf language models by including examples of chain of thought sequences in the prompts, demonstrating the utility of this approach across various reasoning domains.

Example 1: Arithmetic Reasoning

For a math word problem, Chain-of-Thought Prompting helps the model to decompose the problem into smaller, solvable parts, leading to a more accurate final answer. For instance, in a problem about counting tennis balls, the model reasons step-by-step, first calculating the balls in each can and then adding them to the initial count, improving accuracy significantly compared to standard prompting.

Example 2: Commonsense Reasoning

In commonsense reasoning tasks, this method enables the model to logically deduce answers by sequentially reasoning through the given problem. For example, in deciding where someone would go to find people, the model systematically eliminates implausible options to arrive at the correct answer ('populated areas'), showcasing the method's effectiveness in tasks requiring nuanced understanding.

Principled Prompting for ChatGPT

Main Paper Idea
The paper introduces 26 guiding principles to streamline querying and prompting of large language models (LLMs) like LLaMA and GPT-3.5/4. These principles aim to simplify formulating questions for LLMs, examining their abilities, and enhancing user comprehension of different scales of LLMs with various prompts. The approach demonstrates significant improvement in the quality and accuracy of LLM responses.
Principled Prompting for ChatGPT paper image illustration
Why it works
The effectiveness of these principles lies in their focus on optimizing prompts for better interaction with LLMs. By integrating various elements such as audience specificity, task orientation, and clarity, these principles guide LLMs to produce responses that align more closely with user expectations and the context of the query. This approach enhances the precision and relevance of the responses from the LLMs.

Example 1: Simplifying Complex Queries

One principle suggests breaking down complex tasks into simpler prompts for interactive conversations. This application helps in understanding multifaceted concepts by dividing the query into smaller parts, enabling the LLM to provide clearer and more focused responses.

Example 2: Educational Applications

Another principle involves using the LLM to teach a concept and test understanding without providing immediate answers. This can be applied in educational contexts, where the LLM explains a topic and then tests the learner’s understanding, enhancing the educational interaction.

Large Language Models are Human-Level Prompt Engineers

Main Paper Idea
The paper introduces Automatic Prompt Engineer (APE), a method for auto-generating and selecting prompts for Large Language Models (LLMs). APE treats instruction generation as a natural language program synthesis, formulated as a black-box optimization problem. It uses LLMs to generate instruction candidates and selects the best based on a score function, improving LLM performance and often outperforming human-crafted instructions.
Large Language Models are Human-Level Prompt Engineers paper image illustration
Why it works
APE automates prompt engineering, traditionally labor-intensive and needing human expertise. By leveraging LLMs' generative and evaluative capabilities, it explores a vast space of potential instructions, identifying the most effective ones. This approach makes prompt engineering more efficient and potentially more accurate than manual methods.

Example 1: Enhancing Zero-Shot Learning Performance

APE improves zero-shot learning in LLMs by generating and selecting effective prompts, enabling LLMs to better understand and respond to queries they haven't been explicitly trained on, enhancing their generalizability.

Example 2: Improving Few-Shot Learning

In few-shot learning scenarios, APE optimizes prompts to maximize LLMs' learning efficiency with limited data, useful where collecting large training datasets is impractical.

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