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Integrating with LLM Assistance

Use LLMs in your Ideal Postcodes integration workflow.​

We provide documentation in AI-optimized formats to accelerate your development workflow. Feed our comprehensive llms.txt file or individual markdown pages directly into your AI tools for context-aware assistance.

Plain Text Documentation​

Access any documentation page as markdown by adding .md to the URL. For example: https://docs.ideal-postcodes.co.uk/docs/guides/llms.md.

Copy and paste these markdown files directly into your LLM for integration help, code generation, and troubleshooting.

Examples of Plain Text URLs​

https://docs.ideal-postcodes.co.uk/docs/address-finder/address-finder.md
https://docs.ideal-postcodes.co.uk/docs/postcode-lookup/postcode-lookup.md
https://docs.ideal-postcodes.co.uk/docs/api/find-address.md
https://docs.ideal-postcodes.co.uk/docs/integrations/woocommerce.md

LLMs.txt File​

We host an /llms.txt file at https://docs.ideal-postcodes.co.uk/llms.txt which provides a comprehensive, LLM-optimized version of our entire documentation. This file follows the emerging llms.txt standard for making websites and content more accessible to large language models.

What's Included in llms.txt​

Our llms.txt file contains:

  • Complete API Reference: All endpoints, parameters, and response examples
  • Software Library Reference: Address Finder and Postcode Lookup integration documents.
  • Integration Guides: Step-by-step instructions for platforms like WooCommerce, Gravity Forms, and HubSpot
  • Code Examples: JavaScript, HTML, and configuration snippets
  • Best Practices: Security guidelines, optimization tips, and common patterns
  • Troubleshooting: Common issues and their solutions

Using llms.txt with LLM Tools​

The llms.txt format is designed to be directly consumable by AI tools and can be used in various ways:

Direct Copy-Paste: Copy sections or the entire file into your LLM conversation for context-aware assistance.

Tool Integration: Many AI development tools can automatically fetch and use llms.txt files as context.

Custom Workflows: Build automated workflows that reference the llms.txt file for documentation-aware code generation.

Documentation Servers and Context Management​

Using with Context 7​

Our llms.txt file is compatible with documentation servers like Context 7, which can index and serve our documentation to AI tools in a structured way.

Setting up Context 7 with Ideal Postcodes​

  1. Add our documentation source to your Context 7 configuration:

    {
      "sources": [
        {
          "name": "Ideal Postcodes",
          "url": "https://docs.ideal-postcodes.co.uk/llms.txt",
          "type": "llms_txt"
        }
      ]
    }

  2. Query examples you can use with Context 7:

    • "How do I implement Address Finder on a React form?"
    • "What are the security best practices for API keys?"
    • "How do I restrict address search to specific countries?"

Other Documentation Servers​

Our llms.txt file can also be used with other AI-powered documentation tools:

  • Custom RAG Systems: Use as a knowledge base for retrieval-augmented generation

Practical LLM Integration Examples​

Code Generation​

When working with LLMs to generate integration code, provide specific context:

I'm building a checkout form and need to add Address Finder. Here's my current HTML structure:

[paste your form HTML]

Using the Ideal Postcodes documentation at https://docs.ideal-postcodes.co.uk/llms.txt,
help me integrate Address Finder with the following requirements:
- Single line address display
- UK addresses only
- Include UPRN data

Debugging Assistance​

For troubleshooting integration issues:

I'm getting a 402 error when calling the Address Finder API. Here's my code:

[paste your code]

Based on the Ideal Postcodes API documentation, what could be causing this error
and how can I fix it?

Configuration Help​

For complex setup scenarios:

I need to configure API key restrictions for a multi-tenant application where:
- Each tenant has their own subdomain
- I want to limit daily usage per tenant
- Frontend integration on React

Using the Ideal Postcodes security documentation, help me design the proper
API key strategy.

Best Practices for LLM-Assisted Development​

1. Provide Context​

Always include relevant documentation sections when asking for help:

  • Copy the relevant API endpoint documentation
  • Include error messages and response codes
  • Share your current implementation approach

2. Be Specific About Requirements​

Instead of: "Help me add address validation"

Try: "Help me add Address Finder to a multi-step checkout form that needs to validate UK addresses and capture UPRN data for delivery optimization"

3. Iterative Development​

Use LLMs for iterative improvement:

  1. Initial Implementation: Get basic integration working
  2. Enhancement: Add features like country restrictions or additional data
  3. Optimization: Improve performance and user experience
  4. Security: Implement proper API key management

4. Validate Generated Code​

Always review and test LLM-generated code:

  • Check API endpoints and parameters
  • Verify error handling
  • Test with sample data
  • Review security implications

Common LLM Prompts for Ideal Postcodes​

Here are some effective prompts you can use:

Integration Setup​

"Based on the Ideal Postcodes documentation, show me how to integrate Address Finder
into a [framework] application with [specific requirements]"

Error Resolution​

"I'm getting [error code/message] when using the Ideal Postcodes API.
According to the documentation, what are the possible causes and solutions?"

Feature Implementation​

"Using the Ideal Postcodes Address Finder documentation, help me implement
[specific feature] with these requirements: [list requirements]"

Security Configuration​

"Based on the API key security documentation, help me configure proper restrictions
for a [frontend/backend] integration with [specific constraints]"

Getting Started​

  1. Explore the llms.txt file: Visit https://docs.ideal-postcodes.co.uk/llms.txt to see the full documentation
  2. Try markdown URLs: Add .md to any documentation page URL to get the plain text version
  3. Set up your LLM workflow: Choose your preferred AI tool and configure it to use our documentation
  4. Start building: Use the examples and best practices above to accelerate your integration development

Need Help?​

  • Human Support: Contact our technical support team for complex integration questions

While LLMs are powerful development assistants, always validate generated code and consult our official documentation for the most up-to-date information.