Data Extraction Pipeline

Gemini 1.5 Pro is built for production data pipelines: its enormous context window handles long documents in a single call, its structured output mode produces schema-valid JSON reliably, and its pricing scales favorably at the millions-of-tokens volumes that extraction pipelines generate daily.

1. 1

   Define your JSON schema using JSON Schema Draft-07 and pass it directly in the system prompt — models with native JSON mode output schema-valid JSON on the first attempt 95%+ of the time, eliminating parsing failures.

2. 2

   Add a 'confidence' field to every extracted value: instruct the model to output 'high', 'medium', or 'low' confidence per field, and route 'low' confidence extractions to a human review queue rather than auto-ingesting them.

3. 3

   Process documents in parallel workers to maximize throughput — extraction jobs are embarrassingly parallel, and splitting a 10,000-document batch across 20 concurrent workers reduces wall-clock time by 20x without changing cost.