Take an Iterative Approach

When using Nyvo, you don’t have to get everything in one go. In fact, it’s often better to build your dApp step by step with the AI, refining as you go. This iterative development approach means you start with a basic version and gradually enhance it through successive prompts and AI outputs. Here’s how to do it:

• Break down the task: Instead of writing one huge prompt for a very complex dApp, start with a simpler sub-problem. For instance, first ask for the smart contract logic of a feature, or the data schema, or a basic UI without all features. Example: “First, create the Anchor program for the staking logic (without the front-end yet). It should have instructions to stake and unstake tokens and track user balances.” This focuses the AI on a manageable piece.

• Review and test the output: Once you get the code for that piece, review it or even compile/test it if possible. Does it follow Solana’s rules? Does it compile and make sense? Nyvo might allow you to preview the code execution or at least examine the output for errors. If something is wrong or suboptimal, note what needs fixing.

• Refine with a new prompt: Next, prompt the AI to improve or expand the code. For example, “Add an error check to the staking function to prevent overflow of reward calculations,” or “Now create a React UI that calls this staking program (assume the program IDl can be used).” By specifying the improvement or next step, the AI can build on the previous output. This is exactly like pair-programming with the AI – you give it feedback or new requirements, and it writes the next increment of code

• Iterate until complete: Continue this cycle for each component of the dApp. Each iteration should be a small, logical step – this avoids overwhelming the AI and makes it easier to pinpoint issues. For example: after the basic staking program is done, generate the front-end integration. After that, maybe ask the AI to optimize the program for lower compute unit usage or to include unit tests.

The advantage of iterative prompting is that you can catch problems early and adjust course. If the AI made a wrong assumption in one step, you can correct it in the next prompt rather than having to untangle a huge blob of code. This aligns with a key tip: test results after each step. Run or compile the AI-generated code if you can, and if it doesn’t work, use that feedback to guide your next prompt. For example: “The code didn’t account for the account's owner – please update it to check ctx.accounts.token_account.owner is the program ID.” The AI can then incorporate that fix.

Nyvo’s interface is designed for this kind of iterative workflow. You can preview the dApp at each stage, tweak your prompt, and regenerate. Use that to your advantage – treat the AI’s output as a draft that you progressively refine.