DonkeyKong Data Pipeline

Example Use Cases

• 1

  Scalable, distributed data collection with intelligent quality validation.

• 2

  Reduce hallucination in LLM-based data pipelines by separating collection and analysis tasks.

• 3

  Cost-effective local LLM validation combined with cloud LLM analysis.

Description

DonkeyKong implements a distributed data collection system using Docker workers ('Donkeys') for mechanical data gathering, combined with local LLM validation ('Kong') to ensure data quality and adversarial review. It separates tedious data collection from intelligent analysis, reducing hallucination and cloud API costs by using local LLMs for validation and only rerunning low-confidence items with expensive cloud LLMs. The system supports fault tolerance, real-time monitoring, and scalable parallel processing, making it ideal for large-scale, intelligent data pipelines.