Debugging and Refining AI Outputs

Even with great prompts, AI-generated code is rarely perfect on the first try. You should expect to debug and refine the outputs. Here are strategies for improving results and fixing issues:

• Carefully review the AI’s code: Look for common Solana pitfalls. Are all necessary checks and validations present? For example, ensure the AI included signer verification where needed, used correct data types (e.g. u64 for token amounts, not floating point), and respected Solana’s parallel execution (no unnecessary sequential dependencies). Often AI code may miss an overflow check or assume a default that isn’t safe. It’s the user’s job to catch these. Go through each function and line, and consider running Solana unit tests or deploying to a localnet for quick testing.

• Iterate the prompt to fix bugs: If you find a bug or something the AI did wrong, describe the issue in a follow-up prompt. For instance: “The code compiled but fails when an account is already initialized. Modify the initialize_account instruction to return an error if the account’s is_initialized flag is true.” By directly pointing out the problem and desired solution, you guide the AI to patch the code. This is usually faster than editing everything manually, and it teaches the AI about the context for subsequent steps.

• Refine for better quality: Don’t stop at “it just works.” Ask the AI to improve the code quality. This might include optimizing for performance (e.g., reducing compute unit consumption by minimizing loops or unnecessary instructions), improving clarity (adding comments or refactoring into helper functions), or handling edge cases. You can say, for example, “Optimize the stake_tokens function to minimize rent or compute costs, and add comments explaining each step.” The model can then produce a revised version with those changes. Always feel free to ask for an explanation of the code as well if something is unclear – e.g., “Explain how the PDA is derived and used in this code.” This can come in handy if you need to understand or justify the AI’s approach.

• Leverage Nyvo’s preview and testing features: Nyvo lets you preview the generated dApp, which is extremely useful for debugging. After generation, use the preview to interact with the front-end and see if it behaves correctly with the on-chain program. If, say, button clicks aren’t working or transactions fail, inspect the browser console or program logs. Then adjust your prompt accordingly. For example, if the UI didn’t have a connect-wallet button, you might prompt: “Add a wallet connection component (using Solana’s wallet adapter) to the front-end.” Small iterative fixes like this, guided by actual test results, will significantly improve the final dApp.

• Know when to simplify the prompt: If you consistently get poor results, consider that the prompt might be trying to do too much at once. Break it down or simplify the language. Ensure you’re not using ambiguous terms. For instance, the word “account” could mean a user wallet or a Solana account; specifying “Solana account struct” versus “user wallet account” can clear confusion. If the AI output is off-track, sometimes rephrasing the request or providing a quick example of the desired output format can realign it.

Remember, AI is a helper, not a replacement for understanding. Nyvo accelerates development by writing boilerplate and connecting pieces, but you are the architect. Always validate that the AI’s code meets your requirements and Solana’s standards. As one guide puts it, “The AI is just a starting point; you must finish the job”

With vigilant debugging, prompt refinement, and testing, you can converge on a secure and functional Solana dApp.