The Prompt Engineering Assistant Chatbot is a sophisticated tool designed to assist users in crafting effective prompts for Large Language Models (LLMs) like GPT-3 and GPT-4. This project showcases familiarity with various advanced concepts in working with LLMs, including fine-tuning, prompt engineering, data synthesis, automatic evaluation, and retrieval-augmented generation. It serves as a practical example of applying these concepts to create a user-friendly application that enhances interactions with AI models.
- Fine-Tuning LLMs: Demonstrates the process of fine-tuning GPT models on specific datasets to improve performance for targeted tasks.
- Prompt Engineering: Offers insights into crafting prompts that elicit desired responses from LLMs, showcasing techniques for maximizing model output quality.
- Data Synthesis: Utilizes methods for generating synthetic data to enhance model training and provide diverse examples for user guidance.
- Automatic Evaluation: Implements strategies for the automatic evaluation of prompts and model responses, ensuring high-quality feedback.
- Retrieval-Augmented Generation: Explores advanced techniques for augmenting model responses with retrieved information, enhancing the depth and relevance of interactions.
- Prompt Analysis: Analyzes prompts for clarity and specificity, providing actionable feedback.
- Improvement Suggestions: Generates recommendations to refine prompts, leveraging fine-tuned models.
- Examples Repository: Offers a curated collection of effective prompts as a resource for users.
- Best Practices Guide: Shares expert tips on prompt crafting, tailored to various use cases and model capabilities.
- Backend: Fine-tuned GPT-3 or GPT-4 model from OpenAI, showcasing skills in model selection and fine-tuning.
- Frontend: Streamlit, demonstrating the ability to create interactive web applications.
- Deployment: Streamlit Sharing, illustrating knowledge in deploying applications for public access.
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Clone this repository to your local machine.
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Install the required dependencies:
pip install -r requirements.txt
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Create an .env file with your OpenAI API key:
OPENAI_API_KEY=your_api_key_here
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To run the Streamlit application, execute:
streamlit run app/main.py
Enter a prompt into the text area provided on the web interface. The system will analyze the prompt and return feedback along with improvement suggestions. Explore the examples repository and best practices guide to refine your prompt crafting skills.
We welcome feedback and contributions from the community. Feel free to fork the repository, make improvements, and submit pull requests.
This project is released under the MIT License. This revised code block correctly formats the entire README content, ensuring all parts, including setup instructions and how to use the application, are properly included.