promptforge-ai 2.0.0

Enterprise prompt engineering — version control, A/B testing, compression, and linting.

🔨 PromptForge

Enterprise Prompt Engineering Toolkit — Version Control, A/B Testing, and Compression

Manage, version, test, and optimize system prompts at scale. Built for teams shipping LLM-powered products where prompt quality directly impacts revenue.

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The Problem

Your prompt started at 200 tokens and now it's 4,200 tokens because:

• Everyone added "just one more instruction"
• Nobody knows which version performed best
• No A/B testing — changes go live on vibes
• Duplicate instructions bloat cost with zero value

The Solution

from promptforge import PromptRegistry, Prompt, ABTest

# Version-controlled prompts
registry = PromptRegistry("./prompts")

planner_v1 = Prompt(
    name="planner",
    version="1.0",
    template="You are a maintenance planner. Given {query}, create a plan.",
    variables=["query"],
    metadata={"tokens": 45, "author": "naveen"},
)

# Register and track
registry.register(planner_v1)
registry.register(planner_v1.evolve(
    version="1.1",
    template="You plan maintenance tasks. Query: {query}. Output JSON plan.",
    metadata={"tokens": 38, "change": "compressed + JSON output"},
))

# A/B test between versions
test = ABTest(
    name="planner_compression",
    variants={"control": "planner@1.0", "treatment": "planner@1.1"},
    metric="task_success_rate",
    min_samples=100,
)

Features

Feature	Description
Version Control	Git-like prompt versioning with diff, rollback, and blame
A/B Testing	Statistical testing between prompt variants (chi-square, t-test)
Compression	Reduce prompt tokens by 30-60% without quality loss
Template Engine	Variables, conditionals, includes (Jinja2-like)
Cost Scoring	Automatic token counting and per-prompt cost estimation
Diff View	Side-by-side prompt comparison with token impact
Prompt Linting	Detect contradictions, redundancy, and vague instructions
Export	YAML/JSON prompt libraries for CI/CD integration

Quick Start

pip install promptforge

Compression

from promptforge import Compressor

compressor = Compressor()

original = """
You are a maintenance order analyst for SAP systems.
You help plant managers understand the status of their maintenance orders.
You should always be professional and concise in your responses.
When you don't know something, say "I don't have that information".
Never make up data or hallucinate facts about maintenance orders.
Always include the order number in your response.
Format your response using markdown tables when showing multiple items.
"""

compressed = compressor.compress(original)
print(compressed.text)
# "Maintenance order analyst for SAP. Help plant managers with order status.
#  Rules: concise, professional, no fabrication. Include order #. Use markdown tables."

print(compressed.stats)
# {"original_tokens": 89, "compressed_tokens": 34, "savings": "62%", "quality_score": 0.94}

Prompt Linting

from promptforge import Linter

linter = Linter()
issues = linter.check("""
You are helpful. Be concise.
Always provide detailed explanations.
Be brief in your responses.
""")
# [
#   LintIssue(severity="warning", message="Contradicting instructions: 'concise' vs 'detailed explanations'"),
#   LintIssue(severity="info", message="Redundant: 'concise' and 'brief' say the same thing"),
# ]

Architecture

promptforge/
├── registry.py      # Version-controlled prompt store
├── prompt.py        # Prompt model with metadata
├── compressor.py    # Token reduction engine
├── linter.py        # Contradiction & redundancy detection
├── ab_test.py       # Statistical A/B testing
├── template.py      # Variable substitution engine
├── scoring.py       # Quality & cost scoring
└── exporters/       # YAML, JSON, Markdown export

Documentation

• Architecture & Design
• Compression Algorithms
• A/B Testing Guide

License

MIT