> ## Documentation Index > Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Get started with KumoRFM — instant predictions on relational data without model training **KumoRFM is a pre-trained relational foundation model** that generates high-quality predictions directly from your data — no training required. It learns from your existing relational data at query time using in-context learning, enabling fast, production-ready predictions with minimal setup. Prediction tasks are defined using **Predictive Query Language (PQL)**, a lightweight SQL-like interface. You can also use the [Kumo Coding Agent](https://github.com/kumo-ai/kumo-coding-agent) to translate natural language into PQL and iterate on workflows directly in your IDE. Try the [Pre-Trained Quick Start Notebook](https://colab.research.google.com/drive/1v4eQbYmw3xWXX9gT7gPwtEtZU_15YXDH) on Google Colab — run end-to-end with your API key. The KumoRFM SDK workflow follows these steps: Install the SDK, authenticate, and connect to your data sources. Configure your notebook or editor environment for agent-assisted work. Load tables, define data types, and build a relational graph. Write PQL queries and generate instant predictions. Evaluate prediction quality and understand what drives results. Benchmark against RelBench and explore end-to-end examples.