https://github.com/tghurair/agentic-prompting
Leverage large language models (LLMs) and LLM Agents to craft impactful and effective prompts
https://github.com/tghurair/agentic-prompting
agents crewai llms openai streamlit
Last synced: 5 months ago
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Leverage large language models (LLMs) and LLM Agents to craft impactful and effective prompts
- Host: GitHub
- URL: https://github.com/tghurair/agentic-prompting
- Owner: tghurair
- Created: 2024-08-25T14:41:08.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-11T16:35:11.000Z (over 1 year ago)
- Last Synced: 2025-04-08T03:51:07.259Z (about 1 year ago)
- Topics: agents, crewai, llms, openai, streamlit
- Language: Python
- Homepage: https://agentic-prompting.streamlit.app/
- Size: 75.2 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Agentic Prompting: AI Prompt Engineering Assistant
A comprehensive tool designed to help you explore and master various prompt engineering techniques. This project leverages LLMs (Large Language Models) or a LLM agent to optimize and enhance your prompt crafting skills, making it easier to generate effective and context-aware prompts for a wide range of applications.
## Table of Contents
1. [Features](#features)
2. [How to Use](#how-to-use)
3. [Installation](#installation)
4. [Usage](#usage)
5. [Tools Used](#tools-used)
6. [Contributing](#contributing)
## Features
### 1. Agentic Prompting
The Agentic Prompting feature is a sophisticated tool that enhances your prompts through a two-step process:
#### 1. Deep Analysis
The AI agent thoroughly analyzes your prompt idea by:
- Examining the context and intent of your input
- Assessing the complexity of the task
- Identifying key elements and requirements
- Determining the most suitable prompt engineering technique
#### 2. Intelligent Generation
Based on the analysis, the agent crafts an optimized prompt by:
- Applying the chosen prompt engineering technique
- Restructuring and refining the original input
- Enhancing clarity and specificity
- Ensuring alignment with the intended goal
- Providing a detailed explanation of the optimization process
We utilized CrewAI for agent implementation and orchestration, which was instrumental in the Agentic Prompting feature.
### 2. Prompt Engineering Techniques
Explore a variety of prompt engineering techniques, including:
- **General Prompting**: Suitable for open-ended questions and creative tasks.
- **Zero-Shot Prompting**: Ideal for straightforward tasks without examples.
- **Few-Shot Prompting**: Provides examples to guide the model for complex tasks.
- **Include-Exclude Prompting**: Specifies elements to include or exclude in responses.
- **Chain of Thought (CoT)**: Breaks down complex problems into sequential reasoning steps.
- **Chain of Thought Reflection**: Incorporates a reflection step for self-correction.
- **ReAct Prompting**: Combines reasoning and action steps for dynamic interactions.
### 3. Playground
The Playground tab allows you to experiment with different prompts and see how AI models respond in real-time. It provides a sandbox environment to test and refine your prompts, enhancing your understanding of AI behavior and response patterns.
## How to Use
1. **Start with the Prompt Engineering Tab**: Learn about different techniques and practice crafting prompts.
2. **Experiment in the Playground**: Test your prompts with various AI models and refine your skills.
3. **Leverage Agentic Prompting**: Use this feature for advanced optimization of your prompts.
4. **Iterate and Refine**: Continuously improve your prompt crafting skills across all tabs.
## Installation
To get started, clone the repository and install the required dependencies:
```bash
git clone https://github.com/your-repo/agentic-prompting.git
cd agentic-prompting
pip install -r requirements.txt
```
## Usage
You can explore the AI Prompt Engineering Assistant live at [agentic-prompting.streamlit.app](https://agentic-prompting.streamlit.app).
Run the application using locally using Streamlit:
streamlit run app.py
## Tools Used
- Streamlit
- CrewAI
- OpenAI
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs or feature requests.