>Problem & Solution
Problem
Student pilots struggle to learn and navigate the complex web of aviation regulations, laws, and manuals. Traditional teaching methods make it difficult to quickly access and understand specific regulatory information.
Solution
An AI-powered chatbot with dual specialized assistants: Wilbur AI for interpreting the Code of Federal Regulations (FAR) and Orville AI for the Aeronautical Information Manual (AIM). The system allows student pilots to quickly find and understand relevant regulatory information through natural language queries, with a complete web application featuring user authentication, chat history, and responsive design.
>Challenges
Pilot Skepticism Toward AI
Many pilots were dismissive of AI technology, creating a barrier to adoption and requiring additional effort to demonstrate value.
RAG Similarity Search with Aviation Terminology
Aviation terminology contains many similar terms, definitions, and acronyms, making it difficult to perform accurate retrieval of relevant regulations and legislation.
Contextual Understanding of Nested Legal Documents
Aviation regulations often contain similar language in different sections without explicitly stating which type of aircraft they apply to. Understanding requires traversing 'up' the legislation to determine context (e.g., single engine vs. multi-engine aircraft).
Aviation Acronym Comprehension
The extensive use of specialized acronyms in aviation regulations makes understanding the text impossible without proper context. The LLM needed to recognize and interpret these acronyms correctly to provide accurate information.
Pilot Skepticism Toward AI
Many pilots were dismissive of AI technology, creating a barrier to adoption and requiring additional effort to demonstrate value.
RAG Similarity Search with Aviation Terminology
Aviation terminology contains many similar terms, definitions, and acronyms, making it difficult to perform accurate retrieval of relevant regulations and legislation.
Contextual Understanding of Nested Legal Documents
Aviation regulations often contain similar language in different sections without explicitly stating which type of aircraft they apply to. Understanding requires traversing 'up' the legislation to determine context (e.g., single engine vs. multi-engine aircraft).
Aviation Acronym Comprehension
The extensive use of specialized acronyms in aviation regulations makes understanding the text impossible without proper context. The LLM needed to recognize and interpret these acronyms correctly to provide accurate information.
>Approach
Definition Hub Architecture
Created a hierarchical tree/graph structure of legislation where definitions and acronyms are stored at appropriate nodes. When processing a query about a specific regulation, the system traverses from the leaf node (specific regulation) to the root, collecting all applicable definitions and acronyms to provide complete context for the LLM.
Dual Assistant Architecture
Created two specialized AI assistants - Wilbur AI for interpreting the Code of Federal Regulations (FAR) and Orville AI for the Aeronautical Information Manual (AIM) - allowing for more focused expertise in different aspects of aviation regulations.
>Technical Insights
Context-Aware Legal Definition Extraction
Developed a specialized system for extracting and organizing legal definitions and aviation acronyms based on their scope of applicability within the regulatory hierarchy.
Hierarchical Context Traversal
Implemented a system that traverses from specific regulation nodes up through the document hierarchy, collecting relevant definitions and acronyms at each level to provide complete context for the LLM's interpretation.
>Technologies
>Results
- Created a functional prototype with dual AI assistants (Wilbur and Orville) specializing in different aviation regulations
- Successfully implemented a context-aware retrieval system for nested legal documents
- Developed a novel approach to handling aviation acronyms and definitions
- Gained valuable insights about AI adoption challenges in specialized professional fields
>Key Learnings
- AI adoption in specialized fields like aviation requires addressing specific pain points rather than general solutions
- Context-aware retrieval is essential when working with nested legal documents
- Building trust with traditional professionals requires demonstrating concrete value
- Specialized knowledge domains like aviation require custom approaches to information extraction and organization