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Build on Algorand

Web1 and Web2
The Middleman
Blockchain Comes Into Play
Web3 As a New Iteration for Web

What is Algorand?
Development Environment Setup
Introduction to AlgoKit
Environment Setup with AlgoKit
Deploy Smart Contract with AlgoKit
Basic Smart Contract with Python
Basic Smart Contract with TypeScript
Building on Algorand as an Ethereum Developer

Introduction to AI-assisted coding (Installing Addons etc.)
How to Generate a Basic Smart Contract with AI
Deploying to Testnet

Frontend Integration Using AlgoKit and AlgoKit Utils
Integrating Wallet Connection

Final Project: Build and Launch Your Own Algorand dApp

Writing clear and effective prompts is key to getting the best results from AI tools. In this section, you will learn in detail how to craft prompts that help AI generate relevant code, documentation, or explanations for your Algorand projects.

Key Considerations When Writing Prompts

  • Be Clear and Specific: Express what you want as precisely and understandably as possible. For example, instead of “simple counter contract,” say “a contract that maintains a global counter with functions to increment, decrement, and read its value.”
  • Break Down Your Requests: List your needs step-by-step in bullet points rather than long paragraphs. This helps AI understand your requirements better.
  • Describe Functionality and Behavior: Clearly state what the code should do, what inputs it accepts, and what outputs it produces.
  • Include Constraints and Rules: Specify any permissions, access restrictions, limits, or other rules.
  • Specify Desired Output Type: Clearly mention whether you want code, documentation, examples, or explanations.
  • Keep It Concise: Avoid overly long or complicated descriptions; focus on the essential points.
  • Refine Through Feedback: After getting AI’s output, review and adjust your prompt if needed to improve results.

Example Prompt Structure

Here is an example of an effective prompt for an Algorand smart contract:

# Create a counter contract for Algorand.
# The contract will maintain a global variable "count".
# It should include the following functions:
# - increment(): increases the count by 1.
# - decrement(): decreases the count by 1.
# - get_count(): returns the current count value.
# Only authorized users can modify the count.
# Provide the code in Python.

Additional Tips

  • Start your prompt by stating the goal to help AI understand the context correctly.
  • Add usage scenarios or other relevant info if necessary.
  • Request sample inputs and expected outputs to improve quality.
  • Experiment with different versions to find the best prompt.

To effectively leverage AI in your Algorand projects, pay attention to how you write prompts. Clear, structured, and specific prompts enable AI to generate code and documentation with the accuracy and quality you need.

Note: LLMs can occasionally generate incorrect or outdated code. It’s important to always verify the output against official documentation and avoid assuming that code generated by an LLM is automatically correct.

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