Prompt_Engineering_using_Precision_RAG and RAG-Prompt-Generator
Maintenance
0/25
Adoption
3/25
Maturity
16/25
Community
12/25
Maintenance
13/25
Adoption
0/25
Maturity
9/25
Community
0/25
Stars: 3
Forks: 1
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: —
Forks: —
Downloads: —
Commits (30d): 0
Language: Python
License: GPL-3.0
Stale 6m
No Package
No Dependents
No Package
No Dependents
About Prompt_Engineering_using_Precision_RAG
GetachewAbebe/Prompt_Engineering_using_Precision_RAG
This project aims to develop an enterprise-grade Retrieval-Augmented Generation (RAG) system by automating the prompt engineering process. The goal is to create a comprehensive solution that simplifies the task of crafting effective prompts for Language Models (LLMs), enabling businesses to leverage advanced AI capabilities more efficiently.
About RAG-Prompt-Generator
AkshatG09/RAG-Prompt-Generator
A Retrieval-Augmented Generation (RAG) application using FastAPI, ChromaDB, and Qwen 3 to automatically engineer context-aware system prompts from a custom knowledge base.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work