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Document Parsing and Extraction

Getting typed fields out of PDFs, scans and forms is the first stage of most enterprise AI pipelines and the one most pilots never test. Extraction is a ladder (text layer, OCR, layout model, vision model) chosen per page, its output is a schema with a confidence and a location per field, and its accuracy compounds: if field errors were independent, 96% per field would be 61% of twelve-field documents fully correct. The decisions that matter are which rung each page gets, and which fields a human still checks.

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RELATED CONCEPTS

Chunking Strategies Multimodal Models and VLMs Structured Output and Schema Validation Calibration and Uncertainty Bounded Autonomy and Human-in-the-Loop

PRACTICE THIS IN REAL QUESTIONS

ML System Design (Product)Design an evaluation framework for an ads-ranking system.→ML System Design (Product)Design a system to detect bots and inauthentic accounts in real time.→RAG & Agent System DesignHow would you chunk contracts, Slack threads, and PDFs full of tables: same pipeline or different?→Machine Learning & Data ScienceYour model scored 95% in the pilot and 70% in production. What happened?→Machine Learning & Data ScienceExplain k-fold cross-validation, and when would you refuse to use it?→MLOps & ML EngineeringHow do you ensure reproducibility in ML workflows?→