promptgrad 0.1.0

Prompt Sensitivity Analyzer — measure robustness, entropy, and token importance of LLM prompts.

promptgrad

Prompt Sensitivity Analyzer — measure how robust your LLM prompts are to linguistic perturbations.

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Prompt engineering is chaos. The same instruction, phrased slightly differently, can produce wildly different outputs. promptgrad quantifies this instability.

Given a prompt, it:

1. Perturbs it with six linguistic strategies (synonym swap, word deletion, paraphrase, casing, punctuation, sentence reorder)
2. Embeds every variant using TF-IDF (built-in, zero dependencies) or semantic models
3. Computes cosine similarity shifts, output variance, and Shannon entropy
4. Detects unstable instructions and surfaces which tokens drive the instability
5. Outputs a robustness score (0–1), a per-token sensitivity heatmap, and a strategy-level radar chart

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Installation

# Minimal install (TF-IDF embeddings, no ML dependencies)
pip install promptgrad

# With local semantic embeddings (recommended)
pip install "promptgrad[local]"

# With OpenAI embeddings
pip install "promptgrad[openai]"

# With visualisation
pip install "promptgrad[viz]"

# Everything
pip install "promptgrad[all]"

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Quick Start

from promptgrad import PromptAnalyzer

analyzer = PromptAnalyzer()

report = analyzer.analyze("Summarize the document in three sentences.")

print(report.robustness_score)      # e.g. 0.847
print(report.stability_label)       # "STABLE"
print(report.top_sensitive_tokens()) # [("Summarize", 0.91), ("three", 0.72), ...]

for warning in report.warnings:
    print(warning)

Visualise

analyzer.plot(report)                 # All four charts
analyzer.plot(report, kind="heatmap") # Just the token heatmap
analyzer.plot(report, kind="gauge")   # Just the robustness gauge
analyzer.plot(report, save_path="report.png", show=False)

Compare multiple prompts

result = analyzer.compare([
    "Summarize the document in three sentences.",
    "Please provide a summary of the document.",
    "What are the key points in this document?",
])

for prompt, score in result["ranked"]:
    print(f"{score:.3f}  {prompt}")

CLI

# Analyse a prompt
promptgrad analyze "List the top 5 programming languages."

# With semantic embeddings and save plot
promptgrad analyze "Explain quantum computing." \
    --backend sentence_transformers \
    --plot report.png

# Export JSON report
promptgrad analyze "Write a haiku about rain." --json report.json

# Compare prompts from a file (one per line)
promptgrad compare prompts.txt

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Outputs

Field	Type	Description
robustness_score	float	0.0 (unstable) → 1.0 (robust)
stability_label	str	VERY STABLE / STABLE / FRAGILE / UNSTABLE
mean_cosine_similarity	float	Average similarity to original across all perturbations
embedding_shift_std	float	Standard deviation of L2 shifts
entropy	float	Shannon entropy of the similarity distribution
output_variance	float | None	Variance of LLM output embeddings (if provided)
token_importance	dict[str, float]	Token → sensitivity score (0–1)
per_strategy	dict[str, float]	Mean cosine similarity per perturbation type
warnings	list[str]	Human-readable instability warnings

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Embedding Backends

Backend	Requires	Quality	Use when
tfidf	nothing	lexical only	Fast tests, CI, offline
sentence_transformers	pip install promptgrad[local]	semantic	Recommended default
openai	pip install promptgrad[openai] + API key	semantic	Production / highest quality

# Auto-selects sentence_transformers if installed, else tfidf
analyzer = PromptAnalyzer(embedding_backend="auto")

# Explicit
analyzer = PromptAnalyzer(embedding_backend="sentence_transformers",
                          model_name="all-MiniLM-L6-v2")

analyzer = PromptAnalyzer(embedding_backend="openai",
                          model="text-embedding-3-small",
                          api_key="sk-...")

---

Perturbation Strategies

Strategy	What it does
synonym_substitution	Swaps words with near-synonyms
word_deletion	Removes individual non-stopword tokens
paraphrase	Structural rewrites (imperative ↔ question, etc.)
casing_variation	Title, UPPER, lower, mIxEd case
punctuation_variation	Adds, removes, or changes terminal punctuation
order_shuffle	Shuffles sentence order in multi-sentence prompts

Use all of them (default) or pick a subset:

analyzer = PromptAnalyzer(perturbations=["synonym_substitution", "word_deletion"])

Bring your own:

from promptgrad import Perturbation, PerturbationResult

class BackTranslation(Perturbation):
    name = "back_translation"

    def apply(self, prompt, n=5, seed=None):
        # ... call translation API ...
        return [PerturbationResult(original=prompt, perturbed=translated, strategy=self.name)]

analyzer = PromptAnalyzer(perturbations=[BackTranslation()])

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Measuring Output Variance

If you have LLM responses for the perturbed prompts, pass them in to compute semantic output variance:

import openai

client = openai.OpenAI()

def complete(prompt):
    return client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    ).choices[0].message.content

# Generate outputs for each perturbed variant
from promptgrad.perturbations import SynonymSubstitution
perturbed = SynonymSubstitution().apply(my_prompt, n=10)
outputs = [complete(r.perturbed) for r in perturbed]

report = analyzer.analyze(my_prompt, output_texts=outputs)
print(report.output_variance)

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Development

git clone https://github.com/Liodon-AI/promptgrad
cd promptgrad
pip install -e ".[dev]"
pytest -v

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License

MIT © Liodon AI