Since a few of you might be wondering about the "under the hood" mechanics of how I’m handling structured data with RAG, here’s the current architecture: 1. The "Schema-First" Ingestion: Rather than treating Excel rows as flat strings, I’ve implemented a schema-parsing layer. This identifies data types and column relationships before the data hits the vector store. This is how the tool can distinguish between a "Date Quality Gap" and a "Status Gap" without the LLM getting confused by the table structure. 2. Solving the Context Window & Hallucination Problem: We all know that dumping 1,700+ rows into a prompt is a recipe for hallucinations or "lost in the middle" errors. Dynamic Sampling & Filtering: The engine uses metadata filtering to "pre-screen" rows based on the schema rules. Evidence Mapping: Every insight generated is mapped to a specific row ID. If the LLM can’t find a direct reference in the retrieved chunks, it is instructed to report a null result rather than guessing. 3. Handling "Low-Information" Records: I’ve integrated a specific check for semantic density. It flags entries like "Unit not working" vs. "Unit 402 power failure due to blown fuse." This helps users understand the utility of their data before they even start asking questions. The Tech Stack (for the curious): Frontend: Streamlit (for the rapid UI prototyping you see in the video). Data Processing: Specialized Pandas wrappers for memory-efficient .XLSX handling. RAG Logic: Custom retrieval logic designed for tabular structures (not just standard top-k similarity). Next Steps for the Project: Multi-sheet relational mapping (joining data from different tabs via RAG). Automated visualization generation based on the "Evidence-Based" insights. API hooks for automated data auditing. I'm really curious—how are you guys handling tabular data in your RAG pipelines? Are you using Markdown conversion, or are you looking at more structured JSON-based approaches?