WeeklyTalk #152

RAG and ReqIF: Source Connector as a data hub for AI systems

Name: RAG and ReqIF: Source Connector as a data hub for AI systems
Uploaded: 2025-12-11T16:00:00+01:00
Duration: 19 min 34 s
Description: This episode focuses on the technical implementation and challenges of analyzing formal specification documents using AI systems.

This episode focuses on the technical implementation and challenges of analyzing formal specification documents using AI systems.

This episode focuses on the technical implementation and challenges of analyzing formal specification documents (e.g., in the ReqIF standard) using AI systems. We introduce our Source Connector, which acts as a data hub and supplies a RAG (retrieval-augmented generation) system with the relevant, structured source data.

Finally, we highlight the decisive added value of a structured database compared to the sole processing of continuous text for more precise and reliable AI analysis.

00:00:00 - Introduction: Challenges in analyzing formal specifications (ReqIF)
00:00:52 - Clarification of terms: RAG vs. ReqIF
00:01:30 - ChatGPT vs. enterprise AI: Why internal data management is more complex
00:02:45 - Architecture: The role of the source connector as a data hub
00:03:48 - The pipeline: How documents, images, and tables are prepared for the LLM
00:05:13 - In-house development vs. standard solutions (e.g., Microsoft Gateway)
00:06:40 - Structured data vs. free text: The example of “package inserts”
00:08:36 - What is ReqIF? Origin and benefits in industry
00:09:52 - Technical structure of a ReqIF project (XML & attachments)
00:11:55 - Deep dive: How a RAG system answers questions and splits documents (“chunking”)
00:13:35 - Advantages of structured data: More precise answers and source references
00:15:45 - Linking requirements across different documents
00:17:44 - Conclusion and areas of application for ReqIF (automotive, medical technology, software)

December 11, 2025