RAG-Driven-Generative-AI

🥇Gold

@Denis2054

RAG-Driven-Generative-AI offers a robust framework for building Retrieval Augmented Generation applications using LlamaIndex, Deep Lake, and Pinecone. Leverage OpenAI and Hugging Face models for enhanced content generation and evaluation.

5741930Updated Today

Intermediate15 minutes to implementdevelopment

Saves ~300 min per use

Quick InstallView Source

claude install Denis2054/RAG-Driven-Generative-AI

Works with:

ChatGPTGitHub Copilot

Overview

About This Skill

https://github.com/Denis2054/RAG-Driven-Generative-AI/blob/main/CHANGELOG.md

Use Cases

Automate content generation for marketing campaigns to enhance engagement and reach.

Retrieve and analyze data for sales pitches, improving the effectiveness of presentations.

Generate automated reports that summarize key metrics and insights for stakeholders.

Enhance customer support responses by providing accurate and context-aware information.

Best For

GrowthRevOps

Setup & Installation

Quick Install

Terminal

claude install Denis2054/RAG-Driven-Generative-AI

Alternative Install (Git Clone)

git clone https://github.com/Denis2054/RAG-Driven-Generative-AI

Requirements

Claude Code or compatible AI agent
Works with: ChatGPT, GitHub Copilot

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Generate a RAG application for [COMPANY] in the [INDUSTRY] sector that utilizes [DATA] to enhance content creation. Include steps for implementation and potential use cases.

Example Output

### RAG Application for TechCorp in the Software Development Industry

**Overview:**  
TechCorp is developing a RAG application that leverages customer feedback data to improve software documentation.

**Implementation Steps:**  
1. **Data Retrieval:** Use Pinecone to index and retrieve customer feedback from various sources, such as surveys and support tickets.
2. **Content Generation:** Integrate OpenAI's GPT model to generate user-friendly documentation based on the retrieved feedback.
3. **Evaluation:** Employ LlamaIndex to evaluate the generated content against existing documentation for accuracy and relevance.

**Use Cases:**  
- Automating the creation of FAQs based on common customer inquiries.  
- Generating release notes that reflect user feedback on new features.  
- Enhancing onboarding materials with insights from user experiences.