Large Language Models (LLMs) like ChatGPT, Claude, and others can significantly streamline the process of integrating with Nash’s delivery orchestration API. This guide provides a quick overview of how to use LLMs with Nash API effectively.

​
Nash API Documentation for LLMs

When working with LLMs to assist with Nash API integration, provide the AI with comprehensive API documentation: These specially formatted text files contain the Nash API endpoints, schemas, and implementation details in a format that’s optimized for LLMs to process.

​
Example Prompt

I'm working on integrating with Nash's delivery API. Here's the relevant documentation:
https://docs.usenash.com/llms-full.txt

I need to implement [SPECIFIC FUNCTIONALITY]. Can you help me understand how to structure this request?

​
Best Practices for Using LLMs with Nash API

  1. Provide Clear Context: Share the relevant Nash API documentation links with the LLM
  2. Be Specific: Clearly describe your use case and requirements
  3. Include Complete Information: When troubleshooting, share error messages and API responses

​
Key Integration Areas Where LLMs Can Help

  • Understanding Nash Concepts: Orders, Jobs, Batch Jobs, Dispatch Strategies
  • API Request/Response Formatting: Generate code samples for API calls
  • Webhook Implementation: Set up secure webhook handlers for delivery updates
  • Error Handling: Understand and resolve API error responses

​
Security Considerations

  1. Never share API keys or sensitive customer data with LLMs
  2. Always review and test LLM-generated code before deploying to production
For comprehensive integration guidance, refer to our Plan Your Integration documentation.