๐ง AI-Powered Prompt EngineeringApril 10, 2026โ
Tests passing
Prompt Debugger
A focused CLI tool designed to identify weaknesses in AI prompts by testing edge cases and generating diagnostic insights. It analyzes prompt responses for consistency, ambiguity, and sensitivity to wording changes.
What It Does
- Generate edge cases for a given prompt.
- Analyze responses for consistency and ambiguity.
- Provide diagnostic insights and improvement suggestions.
Installation
Install the required Python packages:
pip install openai typer pytestUsage
Run the tool from the command line:
python prompt_debugger.py --prompt "Write a story about a dragon." --model "gpt-3.5-turbo"Source Code
import openai
import typer
from typing import List
def analyze_prompt(prompt: str, model: str) -> str:
"""
Analyzes the given prompt for weaknesses by generating edge cases and evaluating responses.
Args:
prompt (str): The input prompt to analyze.
model (str): The target AI model to use.
Returns:
str: A detailed diagnostic report with improvement suggestions.
"""
try:
# Generate edge cases for the prompt
edge_cases = [
prompt + " (in a humorous tone)",
prompt + " (in one sentence)",
prompt + " (with a twist ending)",
prompt + " (as a poem)",
prompt + " (in the style of Shakespeare)",
]
responses = []
for case in edge_cases:
response = openai.ChatCompletion.create(
model=model,
messages=[{"role": "user", "content": case}]
)
responses.append({"case": case, "response": response["choices"][0]["message"]["content"]})
# Analyze responses for consistency and ambiguity
analysis = []
for item in responses:
response_text = item['response']
if len(response_text.strip()) == 0:
analysis.append(f"Edge case '{item['case']}' produced an empty response.")
elif "error" in response_text.lower():
analysis.append(f"Edge case '{item['case']}' resulted in an error-like response.")
else:
analysis.append(f"Edge case '{item['case']}' produced a valid response.")
# Generate diagnostic report
report = "Prompt Diagnostic Report:\n"
report += "\n".join(analysis)
report += "\n\nSuggestions for Improvement:\n"
report += "- Ensure the prompt is clear and unambiguous.\n"
report += "- Test prompts with different tones and formats to identify weaknesses.\n"
report += "- Avoid overly complex or vague instructions.\n"
return report
except openai.error.OpenAIError as e:
return f"An error occurred while interacting with the OpenAI API: {e}"
app = typer.Typer()
@app.command()
def main(prompt: str = typer.Option(..., help="The prompt to analyze."),
model: str = typer.Option("gpt-3.5-turbo", help="The target AI model to use.")):
"""
CLI entry point for the Prompt Debugger tool.
Args:
prompt (str): The input prompt to analyze.
model (str): The target AI model to use.
"""
report = analyze_prompt(prompt, model)
typer.echo(report)
if __name__ == "__main__":
app()
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Details
- Tool Name
- prompt_debugger
- Category
- AI-Powered Prompt Engineering
- Generated
- April 10, 2026
- Tests
- Passing โ
- Fix Loops
- 2
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-04-10/prompt_debugger cd generated_tools/2026-04-10/prompt_debugger pip install -r requirements.txt 2>/dev/null || true python prompt_debugger.py