LLM Engineering - Learning by Doing

A practical guide to becoming a prompt engineer through hands-on learning with real recruitment scenarios. Perfect for learning LLM prompting over a weekend! This repository was created with help from devin.ai.

Overview

This repository provides a hands-on approach to mastering prompt engineering through practical examples in recruitment scenarios. You'll learn by doing, working through interactive Jupyter notebooks that demonstrate how to:

• Analyze resumes and job descriptions
• Create targeted recruitment prompts
• Generate effective interview questions
• Evaluate candidate responses
• Assess cultural fit

Prerequisites

• Python 3.8 or higher
• Jupyter Notebook
• Ollama
• Basic understanding of LLMs

Setup

1. Install requirements:

pip install jupyter nbformat

2. Install Ollama and download Llama3:

# Follow Ollama installation instructions at https://ollama.ai
ollama pull llama3

3. Run Jupyter:

jupyter notebook

Repository Structure

The repository is organized into three main sections:

1. Introduction of Parameters

Core parameter notebooks for understanding LLM behavior:

• 01_temperature.ipynb: Control randomness and creativity in responses
• 02_top_p.ipynb: Nucleus sampling for balanced text generation
• 03_top_k.ipynb: Token selection strategies for output control
• 04_max_tokens.ipynb: Managing response length effectively
• 05_presence_penalty.ipynb: Reducing repetition in outputs
• 06_frequency_penalty.ipynb: Enhancing response diversity
• 07_stop_sequences.ipynb: Controlling response termination
• 08_context_window.ipynb: Managing input length and context
• 09_system_prompt.ipynb: Defining model behavior and personality
• 10_seed.ipynb: Ensuring reproducible outputs

2. Introduction of Prompting

Fundamental prompting concepts and techniques:

• 11_basic_parameters.ipynb: Essential prompting parameters
• 12_advanced_parameters.ipynb: Advanced parameter combinations
• 13_prompt_structure.ipynb: Building effective prompt templates
• 14_prompt_tasks.ipynb: Task-specific prompting strategies

3. Techniques

Advanced prompting methodologies:

• 15_zero_shot_prompting.ipynb: Prompting without examples
• 16_few_shot_prompting.ipynb: Learning from minimal examples
• 17_chain_of_thought_prompting.ipynb: Step-by-step reasoning
• 18_automatic_reasoning_prompting.ipynb: Self-guided problem solving
• 19_self_consistency_prompting.ipynb: Maintaining consistent outputs
• 20_tree_of_thought_prompting.ipynb: Exploring multiple reasoning paths

Usage Examples

Here's a quick example of how to use the notebooks:

1. Open the desired parameter notebook (e.g., temperature.ipynb)
2. Follow the interactive examples
3. Experiment with different parameter values
4. Observe the effects on model outputs

Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure your PR description clearly describes the changes and links to any relevant issues.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the Ollama team for providing the LLM infrastructure
• Contributors who have helped improve the documentation
• devin.ai for assistance in creating this repository