Case Study: US Air Force achieves faster flight test document generation with Databricks

How Large Language Models Will Revolutionize USAF Flight Test

The US Air Force faced the challenge of bringing large language model capabilities into a secure environment without exposing sensitive information. In its Air Force Test Center Data Hackathon, the team explored how Databricks and the Databricks Lakehouse platform for the U.S. Public Sector could support LLM development using more than 180,000 unclassified flight test documents.

Databricks helped enable a retrieval-augmented generation solution that stored and searched the document corpus in a vector database, then used open-source models to generate USAF-specific outputs. The results showed the LLM could produce useful, context-aware test content and answers that were often indistinguishable from human-written documents, while reducing search and drafting time and pointing to potential savings in cost, errors, and millions of dollars at scale.

US Air Force