This repository provides resources and projects focused on Large Language Models (LLMs), aiming to generate enriched prompts for various search and knowledge platforms. It is intended for researchers and developers interested in the practical application and architecture of LLMs, particularly within the context of generative AI.

How It Works

The project leverages a "category structure" and "word embeddings" derived from Wolfram data to create enriched prompts. The core approach involves processing crawled data, such as crawl_final_stats.txt.gz, using Python scripts within the XLLM5 and XLLM6 folders to facilitate prompt generation for platforms like GPT, Wikipedia, and ArXiv.

Quick Start & Requirements

• Documentation: The primary documentation is available in Projects4.pdf.
• Data: Crawled data is available on Google Drive.
• Prerequisites: Python scripts are mentioned, implying a Python environment is needed.

Highlighted Details

• Focus on prompt enrichment for GPT, Wikipedia, ArXiv, Google Scholar, Stack Exchange, and Google search.
• Explores the "xLLM architecture" and other GenAI applications.
• Includes a specific sub-directory on "Probability & Statistics."
• References a "GenAI Fellowship" free course.

Maintenance & Community

Information regarding maintenance, community channels, or specific contributors is not detailed in the provided README.

Licensing & Compatibility

The license type and compatibility for commercial use or closed-source linking are not specified in the README.

Limitations & Caveats

The repository appears to be a collection of projects and resources rather than a single, runnable application, with a strong emphasis on the accompanying textbook and external course for full understanding and participation.