WeeklyTalk #152
RAG and ReqIF: The Source Connector as a data hub for 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.
Chapter Tokens
00:00 – Introduction: Challenges in analyzing formal specifications (ReqIF)
00:52 – Clarification of terms: RAG vs. ReqIF
01:30 – ChatGPT vs. enterprise AI: Why internal data management is more complex
02:45 – Architecture: The role of the source connector as a data hub
03:48 – The pipeline: How documents, images, and tables are prepared for the LLM
05:13 – In-house development vs. standard solutions (e.g., Microsoft Gateway)
06:40 – Structured data vs. free text: The example of “package inserts”
08:36 – What is ReqIF? Origin and benefits in industry
09:52 – Technical structure of a ReqIF project (XML & attachments)
11:55 – Deep dive: How a RAG system answers questions and splits documents (“chunking”)
13:35 – Advantages of structured data: More precise answers and source references
15:45 – Linking requirements across different documents
17:44 – Conclusion and areas of application for ReqIF (automotive, medical technology, software)
Note on Operation: A single click on the play icon activates both the interactive chapter navigation and the sound of the video.
December 11, 2025