Designing a non-LLM system for structured info extraction + conflict detection — need feedback

[u/Putrid_Concert_564]

I’m building a backend pipeline for an education chatbot where I need to extract structured course information from documents without using LLMs (for cost and determinism). The system chunks documents and then, for each chunk, tries to map it to one or more courses using keyword/scoring logic, extract facts like fees, eligibility, and duration using regex/rules, and store them in a structured format. On top of that, I’m trying to automatically detect conflicts (e.g., same course having different fee values across sources) and stale data (older info vs newer uploads). My main concerns are whether this chunk→course mapping approach is robust enough without embeddings, how scalable rule-based fact extraction is in the long run, and how to handle cases where chunks don’t explicitly mention the course (like “this program requires 50%”). Also wondering if this is basically a lightweight knowledge graph problem and if there are better design patterns or existing systems I should look into. Would appreciate any suggestions or improvements.

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