🧪 LLM Prompt Design Lab

A practical project focused on designing and using prompts with large language models (LLMs). Built with LangChain and Groq's LLaMA 4 Scout model, this project showcases how to structure, template, and execute prompts through CLI and Streamlit web apps.

🧠 Core Highlights

• 🧾 Prompt engineering using LangChain's PromptTemplate
• 🧠 Dynamic prompt execution using Groq-hosted LLaMA-4-Scout-17B
• 💡 Template-driven summarization for research papers
• 🌐 Streamlit-based UI for interactive experimentation
• 💬 CLI chatbot to simulate real-time prompt responses

🛠️ Technologies Used

• LangChain
• Groq
• Streamlit
• python-dotenv
• pyarrow
• langchain_core

📁 File Structure

.
├── chatbot.py              # CLI chatbot using prompt chain
├── prompt.py               # Streamlit web UI with prompt inputs
├── prompt_generator.py     # LangChain PromptTemplate logic
├── requirements.txt
├── template.json           # Saved LangChain prompt template
└── .env                    # Contains your GROQ_API_KEY

🚀 Getting Started

1. Clone the repository

git clone https://github.com/your-username/llm-prompt-design-lab.git
cd llm-prompt-design-lab

2. Install dependencies

pip install -r requirements.txt

3. Set your API Key

Create a .env file with your Groq API key:

GROQ_API_KEY=your_groq_api_key_here

💬 Run CLI Chatbot

python chatbot.py

🌐 Launch Streamlit Web App

streamlit run prompt.py

📌 Example Prompt Use Case

• Title: "GPT-3: Language Models are Few-Shot Learners"
• Style: Technical
• Length: Medium
• Output: Summarized explanation with equations, analogies, and code (if present).

🏷️ Tags

Prompt Engineering, LLM, LangChain, Groq, LLaMA, Streamlit, AI Tools, Research Summarization

👨‍💻 Author

Nitesh Kumar
LinkedIn • GitHub