base prompt for test + code

[oneaiguru]

Creating Effective Prompts to Generate Both Code and Tests

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Based on your request, I've reviewed the provided code and extracted inspiration from the prompts used. Below, I've outlined several potential prompt formulations to instruct the LLM to generate both code and corresponding tests. These prompts are prioritized to help you quickly find an effective one.

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1. Direct Instruction Prompt

Prompt:

"Please update the following code to include necessary imports and ensure functionality. Additionally, generate comprehensive unit tests for the updated code."

Explanation:

• This prompt is straightforward and explicitly asks for both code updates and test generation.
• Inspired by the files_content_prefix and lazy_prompt in your code.

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2. Explicit Code and Test Generation Prompt

Prompt:

"You are a diligent and thorough assistant. For any code you provide, always include the complete implementation along with corresponding unit tests. Do not leave any parts unimplemented or as comments."

Explanation:

• Emphasizes the assistant's role in providing complete implementations and tests.
• Based on the lazy_prompt which encourages full implementation.

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3. Code and Tests in Separate Sections Prompt

Prompt:

"I have added the following files to the chat for you to edit. Please make the necessary changes to the code, and then provide the updated code and its unit tests in separate sections, clearly labeled."

Explanation:

• Mirrors the structure from files_content_prefix and instructs on separating code and tests.
• Helps in organizing the output for clarity.

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4. Emphasizing Complete Implementation Prompt

Prompt:

"You NEVER leave comments describing code without implementing it! Always provide the fully implemented code and the complete set of unit tests needed to verify its correctness."

Explanation:

• Strongly encourages full implementation, inspired by lazy_prompt.
• Reinforces the importance of including tests.

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5. Step-by-Step Code and Test Generation Prompt

Prompt:

"For the code changes requested, first provide the updated code, and then generate the corresponding unit tests. Ensure that the tests cover all important functionalities and edge cases."

Explanation:

• Guides the LLM to output code and tests in sequence.
• Encourages thorough testing.

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6. Use of Code Fencing Prompt

Prompt:

**"Update the code below, and include the unit tests. Use code fences to clearly separate the code and tests, like so:

```python

Updated Code

... ```

```python

Unit Tests

... ```"**

Explanation:

• Instructs the LLM on how to format the response.
• Utilizes the fencing concept from your code to structure output.

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7. Instruction with Emphasis on Completeness Prompt

Prompt:

"Implement all the required code changes and generate all necessary tests. Do not omit any details or leave any placeholders. Ensure that both the code and tests are fully functional and ready to use."

Explanation:

• Emphasizes not leaving any parts unimplemented.
• Encourages readiness for immediate use.

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8. Role-playing Prompt

Prompt:

"You are an expert software engineer who writes both code and tests. When given a code snippet, you will update it as needed and also write comprehensive unit tests to accompany it. Provide both the updated code and the tests."

Explanation:

• Assigns a role to the LLM to encourage appropriate behavior.
• Based on the concept of setting assistant roles in your code.

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9. Reminder of Files to Edit Prompt

Prompt:

"The following files have been added to the chat for you to edit. Trust these as the true contents. Please make the necessary updates to the code and create corresponding unit tests for each file."

Explanation:

• Utilizes files_content_prefix to set context.
• Encourages action on provided files.

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10. Prompt Highlighting Test Generation

Prompt:

"In addition to updating the code, generate all tests for any code you provide. Ensure the tests are thorough and cover all possible cases."

Explanation:

• Directly asks for test generation.
• Inspired by your goal of generating all tests for any provided code.

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Recommendation:

Start with Prompt 1 or Prompt 2, as they explicitly instruct the LLM to generate both code and tests, and are inspired by the lazy_prompt and files_content_prefix in your provided code.

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Additional Tips:

• Be Specific: The more specific you are in your prompt, the better the LLM can fulfill your request.
• Use Clear Language: Phrases like "always include", "do not leave", and "ensure that" help emphasize the importance of certain actions.
• Separate Code and Tests: Ask the LLM to separate the code and tests using headings or code fences for clarity.
• Iterate as Needed: If the initial prompt doesn't yield the desired results, adjust it based on the output you receive.

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Example of Combining Prompts:

You can also combine elements from multiple prompts for a more tailored instruction.

Combined Prompt:

"You are a diligent and thorough assistant. Please update the following code, ensuring all necessary imports and functionality are included. After updating, provide comprehensive unit tests that cover all important functionalities and edge cases. Use code fences to separate the updated code and the tests for clarity."

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I hope these prompts help you quickly find an effective way to instruct the LLM to generate both code and tests. Feel free to adjust them to better fit your specific needs. Let me know if you need further assistance or additional suggestions.

[oneaiguru]

OUTPUT FORMAT

Understood. I'll review the code you've provided, outline the process it performs when executed, and focus on crafting prompts that emphasize the required output format. This way, the LLM will produce code in the specific format your updater expects.

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Overview of the Code Updater Process

The code you've shared seems to be part of a system designed to update code files based on LLM outputs. Here's a breakdown of the process:

1. Parsing Code Blocks

• Format Recognition: The updater scans the input text for code blocks that match a specific pattern.
• Pattern Details:
  • Each code block starts with a line of hashes containing the full file path.

    ########## /full/path/to/file.py ##########

  • The code content for that file follows immediately after this line.

2. Extracting File Paths and Code Content

• File Path Extraction: The updater uses regular expressions to extract the file paths from the header lines.
• Code Content Extraction: It reads the code that follows each header line as the content for the corresponding file.

3. Updating Files

• File Matching: The updater locates the files within the project directory based on the extracted paths.
• File Writing: It overwrites the existing files with the new code content provided by the LLM.

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Emphasis on Output Format

Given this process, it's crucial that the LLM's output adheres strictly to the expected format. The updater relies on this format to correctly parse and update the code files.

Required Output Format

• Structure:

  • Empty Line: Before each file block, include an empty line for clarity.
  • Header Line: A line containing exactly ########## /full/path/to/file.py ##########.
  • Replace /full/path/to/file.py with the actual file path.
  • Code Content: The updated code starts immediately after the header line.

• Example:

  
  ########## /path/to/project/main.py ##########
  # Updated code for main.py
  def new_function():
    pass

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Crafting the Prompt

To ensure the LLM produces output in the required format, the prompt should:

• Explicitly Specify the Format: Clearly describe the exact output structure.
• Avoid Additional Text: Instruct the LLM not to include explanations, comments, or extra text.
• Provide an Example (if necessary): Sometimes, an example can help the LLM understand the format better.

Suggested Prompt

**"Please update the code as specified, ensuring that your output strictly follows this format:

• Start with an empty line.
• For each file, include a header line exactly in this format:

  ########## /full/path/to/file.py ##########

  (Replace /full/path/to/file.py with the actual file path.)

• Immediately after the header line, provide the updated code for that file.
• Do not include any additional text, explanations, or code fences.

Here are the files that need to be updated:

[Insert the code or descriptions of changes needed]

Remember, the correctness of the output format is essential for the updater to process the code correctly."**

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Explanation

• Direct Instructions: The prompt gives clear, step-by-step instructions on how the output should be formatted.
• Emphasis on Format: By highlighting the exact header line format and the need for an empty line before each file block, we ensure the LLM understands the importance of the structure.
• No Extra Text: Instructing the LLM not to include any additional text or explanations minimizes the risk of parsing errors.

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Example of the LLM's Expected Output

Assuming we asked the LLM to update main.py and utils/helper.py, the output should look like:

########## /path/to/project/main.py ##########
# Updated code for main.py
def main():
    print("Hello, World!")

########## /path/to/project/utils/helper.py ##########
# Updated code for helper.py
def helper_function():
    return True

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Additional Tips

• Test the Prompt: Before using it in production, test the prompt to ensure the LLM outputs the code in the correct format.
• Simplify if Needed: If the LLM doesn't adhere to the format, simplify the instructions or provide an explicit example within the prompt.
• Clarify the Importance: Reinforce that adhering to the output format is crucial for the updater to function correctly.

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By focusing on the output format in your prompt and providing clear, explicit instructions, you can guide the LLM to produce code that integrates seamlessly with your updater.

Let me know if you need further assistance or if there's anything else you'd like to clarify.

[oneaiguru]

best for my comments format

Understood. Let's focus on crafting a prompt that instructs the LLM to produce code in your specific format.

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Your Desired Output Format

You prefer the LLM to output code in the following format:

• Multiple files, each separated by a specific header.

• Header Format: A single line comment containing the file path, like:

  # path/to/filename.py

• Code Content: The updated code for the file follows immediately after the header line.

• No Additional Text: No explanations, code fences, or extra comments.

Example Format

# path/to/file1.py
<code for file1>

# path/to/file2.py
<code for file2>

# path/to/file3.py
<code for file3>

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Understanding the Code Updater Process

Your code updater script scans through the LLM's output and looks for lines that start with a # followed by a file path. It then:

• Extracts the File Path: From the comment line.
• Collects the Code: Reads the code following the header until it reaches the next header or the end of the text.
• Updates Files: Overwrites the existing files with the new code content.

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Crafting the Prompt

To ensure the LLM outputs code in the required format, we need to:

• Explicitly Describe the Format: Provide clear instructions about the header and how code should be formatted.
• Instruct to Avoid Extra Text: Emphasize that no additional explanations or code fences should be included.
• Provide an Example: Show the LLM an example of the desired output format.

Suggested Prompt

**"Please update the following code files as specified. Ensure that your output strictly follows this format:

• For each file:
  • Start with a single-line comment containing the file path, like:

    # path/to/filename.py

  • Immediately after the comment, provide the updated code for that file.
• Do not include any additional text, explanations, or code fences.
• Separate multiple files by repeating the header format.

Here are the changes needed:

1. /path/to/file1.py:

   • [Describe the changes or provide the code to update]

2. /path/to/file2.py:

   • [Describe the changes or provide the code to update]

Remember, it's crucial to follow the exact output format so the updater can process the files correctly."**

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Explanation

• Explicit Instructions: Clearly states how each file should be formatted in the output.
• No Extra Text: Instructs the LLM not to include explanations or code fences.
• Separation of Files: By repeating the header format, the updater can distinguish between multiple files.
• Emphasis on Correctness: Highlights the importance of adhering to the format for the updater to work properly.

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Example of Expected LLM Output

Assuming you want to update main.py and utils/helper.py:

# main.py
from pathlib import Path
from typing import Union
from fnmatch import fnmatch

def main():
    print("Hello, World!")

# utils/helper.py
from pathlib import Path
from typing import Union
from fnmatch import fnmatch

def helper_function():
    return True

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Additional Tips

• Test the Prompt: Before running the updater, test the prompt with the LLM to ensure it produces the desired format.
• Simplify if Necessary: If the LLM doesn't follow the format, consider simplifying the instructions or providing a more detailed example.
• Remind About No Code Fences: Since LLMs sometimes default to using code fences, explicitly state not to include them.

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Incorporating Your Existing Code

Since you have a script that updates comments with file names, and the LLM usually provides code in that format, reinforcing this in the prompt will help maintain consistency.

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Summary

By crafting your prompt with clear, explicit instructions and emphasizing the required output format, you can guide the LLM to produce code that aligns with your updater's expectations. This ensures a smoother code update process and minimizes parsing errors.

Let me know if you'd like further assistance or any adjustments to the prompt.