Detect Ambiguous Prompts Using Prompt Perplexity Eval
Effective prompt design is essential for ensuring clear, precise, and contextually relevant AI-generated responses. Ambiguous or overly complex prompts can lead to inconsistent model behaviour, reduced response quality, and difficulty in refining AI interactions.Challenges associated with ambiguous prompts include:
Inconsistent Model Responses – The same prompt may yield unpredictable or varying outputs.
Reduced Comprehensibility – A vague or complex prompt can lead to off-topic, incomplete, or irrelevant responses.
Challenges in Optimization – Poorly structured prompts hinder effective prompt engineering and fine-tuning efforts.
To address these issues, Prompt Perplexity Evaluation measures the clarity and interpretability of a given prompt by assessing the model’s ability to generate a coherent and confident response.This eval quantifies how well a language model understands and processes an input prompt by computing its perplexity score.
Lower Perplexity → The prompt is clear and easy for the model to interpret, leading to consistent and accurate responses.
Higher Perplexity → The prompt is ambiguous, overly complex, or lacks sufficient context, making it difficult for the model to generate coherent responses.
from fi.testcases import TestCasefrom fi.evals.templates import PromptPerplexitytest_case = TestCase( input=''' Can you provide a comprehensive summary of the given text? The summary should cover all the key points and main ideas presented in the original text, while also condensing the information into a concise and easy-to-understand format. Please ensure that the summary includes relevant details and examples that support the main ideas, while avoiding any unnecessary information or repetition. ''')template = PromptPerplexity(config={"model": "gpt-4o-mini"})response = evaluator.evaluate(eval_templates=[template], inputs=[test_case], model_name="turing_flash")print(f"Score: {response.eval_results[0].metrics[0].value[0]}")print(f"Reason: {response.eval_results[0].reason}")