AI-powered Syllabus Extractor
Extracts unstructured course info from PDFs using NLP and regex pipelines.
01
The problem
Each professor has a different way of writing the syllabus, which makes the structure of the document becomes very random. Manual extraction is time-consuming and error-prone, especially when dealing with various PDF formats and layouts.
02
The approach
Prototyped multiple parsing paths under strict LLM-token and latency budgets, benchmarked them, and selected a solution optimized for a serverless backend (low cold-start, low cost) with reliable schema extraction.
03
The result
Successfully extracted course information with 90% accuracy across different PDF formats. Reduced manual processing time from minutes to seconds.
04
The toolkit
- Python
- spaCy
- Regex
- Hugging Face
- LangChain
- LLMs
In practice.
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