Quickstart

Get from zero to a labeled forecasting dataset in a few minutes.

1. Install the SDK

pip install lightningrod-ai

2. Get your API key

Sign up at dashboard.lightningrod.ai to get your API key and $50 of free credits.

3. Generate your first dataset

import lightningrod as lr

client = lr.LightningRod(api_key="your-api-key")
binary_answer = lr.BinaryAnswerType()

# Get AI news to train a domain expert
seeds = lr.NewsSeedGenerator(
    start_date="2025-01-01",
    end_date="2025-04-01",
    search_query=["frontier AI model", "AI agents", "open source LLM", "AI research"],
)

# Define the scope and style of the questions
questioner = lr.ForwardLookingQuestionGenerator(
    instructions="Write forward-looking, self-contained questions with explicit dates/entities.",
    examples=["Will OpenAI publicly release GPT-5 by March 15, 2026?"],
    answer_type=binary_answer,
)

# Verify answers against live sources
labeler = lr.WebSearchLabeler(answer_type=binary_answer)

# Run pipeline
pipeline = lr.QuestionPipeline(seed_generator=seeds, question_generator=questioner, labeler=labeler)
dataset = client.transforms.run(pipeline, max_questions=1000)

The pipeline fetches news seeds, generates forecasting questions, labels them via web search, and returns a dataset.

Where to go next

• Dataset Generation Overview — Understand pipelines, seed generators, and question types

• Forecasting Overview — Get probability estimates with foresight-v3 forecasting model

• Fine Tuning Overview — Fine-tune models on your datasets (early access)