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description Master the art of writing effective prompts for Zoo Code. Learn principles, techniques, and examples to get better AI coding assistance results.
keywords
prompt engineering
AI prompts
effective communication
Zoo Code tips
custom instructions

Prompt Engineering Tips

Prompt engineering is the art of crafting effective instructions for AI models like Zoo Code. Well-written prompts lead to better results, fewer errors, and a more efficient workflow.

General Principles

  • Be Clear and Specific: Clearly state what you want Zoo Code to do. Avoid ambiguity.

    • Bad: Fix the code.
    • Good: Fix the bug in the calculateTotal function that causes it to return incorrect results.

  • Provide Context: Use Context Mentions to refer to specific files, folders, or problems.

    • Good: @/src/utils.ts Refactor the calculateTotal function to use async/await.

  • Break Down Tasks: Divide complex tasks into smaller, well-defined steps.

  • Give Examples: If you have a specific coding style or pattern in mind, provide examples.

  • Specify Output Format: If you need the output in a particular format (e.g., JSON, Markdown), specify it in the prompt.

  • Iterate: Don't be afraid to refine your prompt if the initial results aren't what you expect.

Thinking vs. Doing

It's often helpful to guide Zoo Code through a "think-then-do" process:

  1. Analyze: Ask Zoo Code to analyze the current code, identify problems, or plan the approach.
  2. Plan: Have Zoo Code outline the steps it will take to complete the task.
  3. Execute: Instruct Zoo Code to implement the plan, one step at a time.
  4. Review: Carefully review the results of each step before proceeding.

Using Custom Instructions

You can provide custom instructions to further tailor Zoo Code's behavior. There are two types of custom instructions:

  • Global Custom Instructions: Apply to all modes.
  • Mode-Specific Custom Instructions: Apply only to a specific mode (e.g., Code, Architect, Ask, Debug, or a custom mode).

Custom instructions are added to the system prompt, providing persistent guidance to the AI model. You can use these to:

  • Enforce coding style guidelines.
  • Specify preferred libraries or frameworks.
  • Define project-specific conventions.
  • Adjust Zoo Code's tone or personality.

See the Custom Instructions section for more details.

Handling Ambiguity

If your request is ambiguous or lacks sufficient detail, Zoo Code might:

  • Make Assumptions: It might proceed based on its best guess, which may not be what you intended.
  • Ask Follow-Up Questions: It might use the ask_followup_question tool to clarify your request.

It's generally better to provide clear and specific instructions from the start to avoid unnecessary back-and-forth.

Providing Feedback

If Zoo Code doesn't produce the desired results, you can provide feedback by:

  • Rejecting Actions: Click the "Reject" button when Zoo Code proposes an action you don't want.
  • Providing Explanations: When rejecting, explain why you're rejecting the action. This helps Zoo Code learn from its mistakes.
  • Rewording Your Request: Try rephrasing your initial task or providing more specific instructions.
  • Manually Correcting: If there are a few small issues, you can also directly modify the code before accepting the changes.

Examples

Good Prompt:

@/src/components/Button.tsx Refactor the Button component to use the useState hook instead of the useReducer hook.

Bad Prompt:

Fix the button.

Good Prompt:

Create a new file named utils.py and add a function called calculate_average that takes a list of numbers and returns their average.

Bad Prompt:

Write some Python code.

Good Prompt:

@problems Address all errors and warnings in the current file.

Bad Prompt:

Fix everything.

By following these tips, you can write effective prompts that get the most out of Zoo Code's capabilities.