# Examples

Run our example notebooks in Google Colab to explore the SDK. Quick Start gets you running; Getting Started covers core concepts; Custom Filesets, Answer Types, Evaluation, and Fine Tuning show workflows for specific use cases.

## Quick Start

<table data-view="cards"><thead><tr><th>Example</th><th data-hidden data-card-target data-type="content-ref">Open in Colab</th></tr></thead><tbody><tr><td>Quick Start</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/00_quickstart.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/00_quickstart.ipynb</a></td></tr></tbody></table>

## Getting Started

Step-by-step guides for datasources, answer types, GRPO and SFT training.

<table data-view="cards"><thead><tr><th>Example</th><th data-hidden data-card-target data-type="content-ref">Open in Colab</th></tr></thead><tbody><tr><td>News Datasource</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/01_news_datasource.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/01_news_datasource.ipynb</a></td></tr><tr><td>Custom Documents</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/02_custom_documents_datasource.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/02_custom_documents_datasource.ipynb</a></td></tr><tr><td>BigQuery Datasource</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/03_bigquery_datasource.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/03_bigquery_datasource.ipynb</a></td></tr><tr><td>Answer Types</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/04_answer_types.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/04_answer_types.ipynb</a></td></tr><tr><td>GRPO Training</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/05_grpo_training.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/05_grpo_training.ipynb</a></td></tr><tr><td>SFT Training</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/06_sft_training.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/getting_started/06_sft_training.ipynb</a></td></tr></tbody></table>

## Custom Filesets

Create custom filesets and generate QA pairs from your own documents.

<table data-view="cards"><thead><tr><th>Example</th><th data-hidden data-card-target data-type="content-ref">Open in Colab</th></tr></thead><tbody><tr><td>Create Fileset</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/01_create_fileset.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/01_create_fileset.ipynb</a></td></tr><tr><td>Basic QA Generation</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/02_basic_qa_generation.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/02_basic_qa_generation.ipynb</a></td></tr><tr><td>Advanced Features</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/03_advanced_features.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/03_advanced_features.ipynb</a></td></tr><tr><td>Beige Book (Document Labeling)</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/04_beige_book_e2e.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/custom_filesets/04_beige_book_e2e.ipynb</a></td></tr></tbody></table>

## Answer Types

Focused examples for binary, continuous, and multiple-choice answer type workflows.

<table data-view="cards"><thead><tr><th>Example</th><th data-hidden data-card-target data-type="content-ref">Open in Colab</th></tr></thead><tbody><tr><td>Binary</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/answer_types/binary.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/answer_types/binary.ipynb</a></td></tr><tr><td>Continuous</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/answer_types/continuous.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/answer_types/continuous.ipynb</a></td></tr><tr><td>Multiple Choice</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/answer_types/multi-choice.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/answer_types/multi-choice.ipynb</a></td></tr></tbody></table>

## Evaluation

Evaluate models, run consensus analysis, and backtest on prediction markets.

<table data-view="cards"><thead><tr><th>Example</th><th data-hidden data-card-target data-type="content-ref">Open in Colab</th></tr></thead><tbody><tr><td>Foresight Model</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/01_foresight_model.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/01_foresight_model.ipynb</a></td></tr><tr><td>Model Consensus</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/02_model_consensus.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/02_model_consensus.ipynb</a></td></tr><tr><td>Polymarket Backtesting</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/03_polymarket_backtesting.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/03_polymarket_backtesting.ipynb</a></td></tr><tr><td>Document Classification</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/04_document_classification.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/evaluation/04_document_classification.ipynb</a></td></tr></tbody></table>

## Fine Tuning

Full workflows from seeds to labeled datasets and fine-tuned models.

<table data-view="cards"><thead><tr><th>Example</th><th data-hidden data-card-target data-type="content-ref">Open in Colab</th></tr></thead><tbody><tr><td>Golf Forecasting</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/01_golf_forecasting.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/01_golf_forecasting.ipynb</a></td></tr><tr><td>Trump Forecasting</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/02_trump_forecasting.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/02_trump_forecasting.ipynb</a></td></tr><tr><td>Survival LLM</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/03_survival_llm.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/03_survival_llm.ipynb</a></td></tr><tr><td>Military Strikes Forecasting</td><td><a href="https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/04_military_strikes.ipynb">https://colab.research.google.com/github/lightning-rod-labs/lightningrod-python-sdk/blob/main/notebooks/fine_tuning/04_military_strikes.ipynb</a></td></tr></tbody></table>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.lightningrod.ai/python-sdk/getting-started/examples.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.