Developer’s Guide to Building with AI
Learn practical AI prompting techniques to build confidential smart contracts and integrate FHE into your development workflow.This guide helps developers leverage AI tools effectively when building with Fhenix. Whether you’re using Cursor, GitHub Copilot, or other AI assistants, these strategies will help you get better results and integrate AI smoothly into your FHE development process.
Understanding Context Windows
Why Context Matters
AI coding assistants have what’s called a “context window” - the amount of text they can “see” and consider when generating responses. Think of it as the AI’s working memory:- Most modern AI assistants can process thousands of tokens (roughly 4-5 words per token)
- Everything you share and everything the AI responds with consumes this limited space
- Once the context window fills up, parts of your conversational history may be lost
Optimizing for Context Windows
To get the most out of AI assistants when building with Fhenix:- Prioritize relevant information: Focus on sharing the most important details about your FHE use case first
- Remove unnecessary content: Avoid pasting irrelevant code or documentation
- Structure your requests: Use clear sections and formatting to make information easy to process
- Reference Fhenix docs: Share specific documentation links or code snippets relevant to your task
- Create a project summary: For larger projects, maintain a central documentation file that summarizes key FHE patterns and encryption strategies
Setting Up AI Tools
Configuring Cursor Rules
Cursor Rules allow you to provide consistent context to Cursor AI, making it more effective at understanding your Fhenix codebase and providing relevant suggestions.Creating Cursor Rules
-
Open the Command Palette in Cursor:
- Mac:
Cmd + Shift + P - Windows/Linux:
Ctrl + Shift + P
- Mac:
- Search for “Cursor Rules” and select the option to create or edit rules
-
Add project-specific rules that help Cursor understand your Fhenix project:
- Specify that you’re using Fhenix for confidential smart contracts
- Include your preferred Solidity version and patterns
- Note any specific FHE operations you’ll be using
- Save your rules file and Cursor will apply these rules to its AI suggestions
Creating Project Documentation
A comprehensive instructions file helps AI tools understand your Fhenix project better. This should be created early in your project and updated regularly. Ready-to-Use Prompt for Creating Instructions.md:Effective Prompting Strategies
Be Specific and Direct
Start with clear commands and be specific about what you want. AI tools respond best to clear, direct instructions. Example: ❌ “Help me with my Fhenix code” ✅ “Create a confidential voting smart contract using Fhenix FHE that encrypts votes and allows tallying without revealing individual votes”Provide Context for Complex Tasks
Ready-to-Use Prompt:Ask for Iterations
Start simple and refine through iterations rather than trying to get everything perfect in one go. Ready-to-Use Prompt:Working with Fhenix
Leveraging Fhenix Documentation
When building with Fhenix, it’s important to provide AI assistants with the right context about FHE operations and patterns. Example FHE Implementation Prompt:Component Integration Example
Ready-to-Use Prompt for Confidential Balance Display:Debugging with AI
Effective Debugging Prompts
Ready-to-Use Prompt for Bug Analysis:When You’re Stuck
If you’re uncertain how to proceed: Ready-to-Use Clarification Prompt:Advanced Prompting Techniques
Modern AI assistants have capabilities that you can leverage with these advanced techniques: 1. Step-by-step reasoning: Ask the AI to work through FHE problems systematicallyBest Practices Summary
- Understand context limitations: Recognize that AI tools have finite context windows and prioritize information accordingly
- Provide relevant context: Share Fhenix-specific code snippets, encryption patterns, and project details that matter for your specific question
- Be specific in requests: Clear, direct instructions about FHE operations yield better results than vague questions
- Break complex tasks into steps: Iterative approaches often work better for complex FHE implementations
- Request explanations: Ask the AI to explain generated code or FHE concepts you don’t understand
- Use formatting for clarity: Structure your prompts with clear sections and formatting
- Reference Fhenix documentation: When working with FHE, share relevant documentation links or examples
- Test and validate: Always review and test AI-generated FHE code before implementing in production
- Build on previous context: Refer to earlier parts of your conversation when iterating on FHE implementations
- Provide feedback: Let the AI know what worked and what didn’t to improve future responses