Prompt_Engineering and prompt-engineering-design

Prompt_Engineering
64
Established
Maintenance 17/25
Adoption 10/25
Maturity 16/25
Community 21/25
Maintenance 6/25
Adoption 6/25
Maturity 8/25
Community 15/25
Stars: 7,253
Forks: 934
Downloads:
Commits (30d): 9
Language: Jupyter Notebook
License:
Stars: 17
Forks: 4
Downloads:
Commits (30d): 0
Language: Python
License:
No Package No Dependents
No License No Package No Dependents

About Prompt_Engineering

NirDiamant/Prompt_Engineering

This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.

This project provides tutorials and practical examples for crafting effective instructions to large language models (LLMs). It helps AI developers and practitioners learn how to structure their input so that AI models produce more accurate, relevant, and useful outputs. You'll find guidance on what to include in your prompts and how to refine them for better results.

AI-development natural-language-processing machine-learning-engineering LLM-fine-tuning

About prompt-engineering-design

xlisp/prompt-engineering-design

Prompt engineering design for LLM

This project helps anyone working with Large Language Models (LLMs) to design better prompts, moving beyond simple queries to more structured and reliable interactions. It takes your problem description and desired outcomes, guiding the LLM to produce accurate, contextually relevant, and consistently formatted results. Data scientists, AI engineers, and product managers looking to get the most out of LLMs for specific tasks would find this valuable.

AI product development conversational AI natural language processing workflow automation AI-assisted coding

Scores updated daily from GitHub, PyPI, and npm data. How scores work