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TL;DR

Catalog Comparison RAG is a focused retrieval-augmented generation project designed to compare catalog information and extract meaningful insights from structured and unstructured product data. Leveraging Python and notebook-based experimentation, the project demonstrates how LLM-powered reasoning can be grounded in relevant catalog context for more accurate comparisons and Q&A workflows.

Introduction

In many real-world scenarios, product catalogs are large, fragmented, and difficult to compare manually. This project explores a RAG-style approach where data is retrieved from relevant catalog sources and used as context for an LLM to answer questions, summarize differences, and highlight insights. The goal is to make catalog analysis more intelligent, explainable, and operationally useful.

🚀 Key Features

✅ Retrieval-augmented generation for contextual answer generation
📦 Catalog comparison workflows built around relevant product data
🧠 Semantic retrieval patterns for focused context selection
📊 Notebook-driven exploratory analysis and prototype development
🔍 Better grounding of AI responses in real catalog information
📈 Practical example of combining LLMs with domain-specific knowledge retrieval

🛠️ Tech Stack

• Language: Python
• Environment: Jupyter Notebook
• AI Workflow: Retrieval-Augmented Generation (RAG)
• Data Processing: Python-based retrieval and analysis pipeline
• Research / Experimentation: Notebook-first prototyping

📸 Screenshots

📚 How to Use

To explore the project locally:

1. Clone the repository
   bash

   git clone https://github.com/Tr1ck-5t3r/catalog-comparison-rag.git
   cd catalog-comparison-rag

2. Create a Python environment and install the requirements:
   bash

   python -m venv .venv
   source .venv/bin/activate
   pip install -r requirements.txt

3. Open the notebook files in Jupyter and execute the catalog comparison workflow.
4. Feed relevant data into the retrieval pipeline and use the generated outputs to answer domain-specific questions.

Conclusion

Catalog Comparison RAG demonstrates how AI can be applied in a practical, domain-oriented way by combining retrieval systems with language models. The project highlights my interest in building intelligent workflows that are grounded in real-world data and designed to help users compare, reason, and make informed decisions more effectively.