bootstrap

Bootstrap

Cold-start an evaluation system when you have nothing. In 30 minutes you get a working v0 grader and a clear path to a calibrated, trustworthy evaluation.

Requires OpenJudge (pip install py-openjudge) for the grader generators (SimpleRubricsGenerator / IterativeRubricsGenerator). The interview, stratification, and calibration-roadmap methodology is SDK-independent.

Checklist

You MUST create a task for each item and complete them in order:

1. Understand the product — one-shot interview, not question-by-question
2. Generate v0 grader — use OpenJudge SimpleRubricsGenerator
3. Synthesize eval inputs — 30 inputs with 60/30/10 stratification
4. Run v0 evaluation — GradingRunner with the generated grader
5. Output roadmap — exactly how to reach 50 labels → calibrate

Step 1: Product Interview (One Shot)

Ask the user to describe their system in one go:

To bootstrap your evaluation, I need to understand what you're building.
Please describe (all at once):

- What does your system do? Who uses it?
- What are 3 examples of perfect outputs?
- What are 3 things the system must never do?
- What failures worry you most?

Don't drip-feed these questions. One prompt, one answer. If the user provides a spec doc or design document instead, read that directly.

Step 2: Generate v0 Grader

Use OpenJudge's SimpleRubricsGenerator to create a zero-shot grader from the product description:

import asyncio
from openjudge.models.openai_chat_model import OpenAIChatModel
from openjudge.generator.simple_rubric.generator import (
    SimpleRubricsGenerator,
    SimpleRubricsGeneratorConfig,
)
from openjudge.runner.grading_runner import GradingRunner

# OpenAIChatModel reads OPENAI_API_KEY / OPENAI_BASE_URL from the environment.
# For Aliyun DashScope (Bailian): set OPENAI_BASE_URL to
# https://dashscope.aliyuncs.com/compatible-mode/v1 and OPENAI_API_KEY to your key.
model = OpenAIChatModel(model="qwen-plus")  # or "gpt-4o", etc.

config = SimpleRubricsGeneratorConfig(
    grader_name="Initial Quality Grader",
    model=model,
    task_description="<summarize from the interview>",
    scenario="<usage context from interview>",
    min_score=0,
    max_score=1,
)

generator = SimpleRubricsGenerator(config)
grader = await generator.generate(
    dataset=[],
    sample_queries=[
        "<example query 1 from interview>",
        "<example query 2 from interview>",
        "<example query 3 from interview>",
    ],
)