Introducing ATSC-RAG: An AI-Powered Knowledge Assistant for ATSC 3.0 Specifications

Claude Desktop querying ATSC 3.0 specs via ATSC-RAG MCP server integration
Claude Desktop querying ATSC 3.0 specs via ATSC-RAG MCP server integration

Introducing ATSC-RAG

I’ve been working on a project that I’m excited to share with the ATSC community: ATSC-RAG, a Retrieval-Augmented Generation (RAG) system designed specifically for querying ATSC 3.0 and B2X specifications.

The system enables intelligent querying of broadcast standards documentation using modern AI techniques. Instead of manually searching through hundreds of pages of specifications, you can simply ask questions in natural language and get accurate, cited answers.

How It Works

ATSC-RAG combines several technologies to deliver authoritative answers:

  • Large Language Model: Qwen3-14B with 32K context window for understanding and generating responses
  • Vision Language Model: Qwen3-VL-30B for analyzing diagrams and visual content from specifications
  • Vector Database: Milvus storing approximately 40,000 document chunks for similarity search
  • MCP Server: Integration with Claude Code and other AI assistants via Model Context Protocol

When you ask a question like “What is a PLP in ATSC 3.0?”, the system retrieves relevant sections from the indexed specifications, then generates a comprehensive answer with source citations pointing to specific documents and page numbers.

The system runs entirely in Docker containers, making deployment straightforward for anyone who wants to set it up.

Value to the ATSC Community

Working with broadcast standards can be challenging. The ATSC 3.0 suite includes dozens of specifications covering physical layer, link layer, signaling, video, audio, security, and more. Finding the right information often means searching across multiple documents.

ATSC-RAG addresses this by:

  • Making specifications searchable: Ask questions in plain English and get relevant answers
  • Providing source citations: Every answer includes references to specific documents and pages
  • Supporting visual content: Diagrams and figures are analyzed and indexed alongside text
  • Integrating with AI tools: The MCP server allows Claude Code and other AI assistants to query specifications directly

For engineers, researchers, and anyone working with ATSC 3.0, this can significantly reduce the time spent hunting for information.

Looking for Contributors

I’ve developed this system to help with my own work in the ATSC community, but I believe it could benefit many others. The project is currently hosted at github.com/jp127266/atsc-rag, a personal repo.

I’m open to contributors who are interested in:

  • Improving the RAG pipeline and answer quality
  • Adding support for additional document formats
  • Implementing Phase 2 (GraphRAG for relationship queries)
  • Implementing Phase 3 (Ontology)
  • Testing and providing feedback
  • Documentation and examples

With enough community interest and contributions, I hope to fully open-source this project for the benefit of the entire ATSC community. If you’re working with ATSC 3.0 specifications and interested in AI-powered tools, I’d love to hear from you.