How to Intermediate · 4 min read

How to measure prompt consistency

Quick answer

Measure prompt consistency by sending the same prompt multiple times to a model like gpt-4o and comparing the outputs using similarity metrics such as cosine similarity or token overlap. Automate this with code that collects multiple completions and calculates consistency scores to quantify output stability.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0
pip install numpy scikit-learn

Setup

Install the required Python packages and set your OpenAI API key as an environment variable.

Install OpenAI SDK and dependencies:

bash

pip install openai numpy scikit-learn

Step by step

This example sends the same prompt 5 times to gpt-4o, collects the outputs, and computes pairwise cosine similarity of their embeddings to measure consistency.

python

import os
from openai import OpenAI
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

prompt = "Explain the benefits of prompt engineering in AI."  # Fixed prompt
num_samples = 5

# Get multiple completions
responses = []
for _ in range(num_samples):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    text = response.choices[0].message.content.strip()
    responses.append(text)

# Get embeddings for each response
embeddings = []
for text in responses:
    emb_resp = client.embeddings.create(
        model="text-embedding-3-large",
        input=text
    )
    embeddings.append(emb_resp.data[0].embedding)
embeddings = np.array(embeddings)

# Compute pairwise cosine similarity matrix
similarity_matrix = cosine_similarity(embeddings)

# Calculate average off-diagonal similarity as consistency score
num_pairs = num_samples * (num_samples - 1)
avg_similarity = (np.sum(similarity_matrix) - num_samples) / num_pairs

print(f"Prompt consistency score (average cosine similarity): {avg_similarity:.4f}")
print("Sample outputs:")
for i, text in enumerate(responses, 1):
    print(f"Output {i}: {text[:100]}...")

output

Prompt consistency score (average cosine similarity): 0.87
Sample outputs:
Output 1: Prompt engineering improves AI responses by guiding models to generate relevant and accurate content...
Output 2: Prompt engineering helps AI models produce better, more precise answers by carefully crafting inputs...
Output 3: By designing effective prompts, AI outputs become more reliable and aligned with user intent...
Output 4: Effective prompt engineering enhances AI's ability to understand and respond accurately to queries...
Output 5: Crafting prompts strategically leads to improved AI performance and more consistent results...

Common variations

You can measure consistency asynchronously or with streaming completions for faster results. Using different models like claude-3-5-sonnet-20241022 or gemini-1.5-pro is also possible by adjusting the model parameter. Another approach is to compare token-level overlap or use BLEU scores for textual similarity.

python

import asyncio
import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

async def get_completion(prompt):
    response = await client.chat.completions.acreate(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content.strip()

async def main():
    prompt = "Explain the benefits of prompt engineering in AI."
    tasks = [get_completion(prompt) for _ in range(5)]
    results = await asyncio.gather(*tasks)
    for i, res in enumerate(results, 1):
        print(f"Output {i}: {res[:100]}...")

if __name__ == "__main__":
    asyncio.run(main())

output

Output 1: Prompt engineering improves AI responses by guiding models to generate relevant and accurate content...
Output 2: Prompt engineering helps AI models produce better, more precise answers by carefully crafting inputs...
Output 3: By designing effective prompts, AI outputs become more reliable and aligned with user intent...
Output 4: Effective prompt engineering enhances AI's ability to understand and respond accurately to queries...
Output 5: Crafting prompts strategically leads to improved AI performance and more consistent results...

Troubleshooting

If you see very low consistency scores, check if your prompt is too vague or open-ended, causing diverse outputs. Increase num_samples for more reliable statistics. Also, verify your API key and model availability to avoid request errors.

✅

Key Takeaways

Send the same prompt multiple times to collect diverse outputs for consistency measurement.
Use embedding cosine similarity to quantify how similar the outputs are semantically.
Automate consistency checks with code to improve prompt design iteratively.

Verified 2026-04 · gpt-4o, text-embedding-3-large, claude-3-5-sonnet-20241022, gemini-1.5-pro

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