Start by clarifying requirements and scale, then propose a high-level architecture that separates document ingestion, AI-powered field extraction, and a hybrid search layer. Emphasize trade-offs in the AI pipeline (accuracy vs. latency/cost) and how you'd handle failures and reprocessing. Conclude with how you'd expose the search via APIs and ensure consistency between extracted fields and free-text indexes.
Pro tip: Show awareness that AI extraction is probabilistic: design for confidence scores, human-in-the-loop review for low-confidence fields, and idempotent reprocessing when models improve. This demonstrates production maturity beyond just wiring up an LLM.
Ask about document types, expected volume, latency requirements, and search expectations. Establish whether extraction is synchronous or asynchronous and what accuracy is acceptable.
Outline a pipeline where uploaded documents are stored in object storage, metadata in a relational DB, and raw text in a search index. Use a queue to decouple upload from processing.
Break extraction into stages: OCR/parsing, entity recognition, field mapping, and validation. Discuss using LLMs or specialized models per stage, with confidence scores and fallback logic.
Combine structured field queries (e.g., SQL or Elasticsearch filters) with free-text search (e.g., BM25 or vector search). Explain how extracted fields are indexed and kept in sync.
Discuss latency vs. accuracy, cost of AI calls, handling extraction failures, and reprocessing documents when models improve. Mention monitoring and human review for low-confidence extractions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.