> ## Documentation Index
> Fetch the complete documentation index at: https://docs.contextual.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Generate

> Contextual AI Component Quickstart

## Overview

By accessing Contextual AI's Generate API component, you can generate responses using Contextual AI's Grounded Language Model (GLM), an LLM engineered specifically to prioritize faithfulness to in-context retrievals over parametric knowledge to reduce hallucinations in retrieval-augmented generation (RAG) and agentic use cases.

## Key Features

* State-of-the-art groundedness with top performance on FACTS and real enterprise benchmarks.
* Hallucination-resistant generation that prioritizes retrieved knowledge over training data.
* Inline source attributions for transparent, traceable responses.
* Reliable multi-turn reasoning that maintains grounding in long or complex workflows.
* RAG-optimized design built specifically for retrieval and agentic applications.
* Configurable groundedness via the avoid\_commentary flag.
* Easy integration through `/generate`, Python SDK, and LangChain examples.
* Enhanced performance when used with the Contextual AI Platform’s unified RAG stack.

## Getting Started

See the [Generate How-to guide](/how-to-guides/generate) for a detailed walkthrough on how to use the Generate API.

## Additional Resources

* [Contextual AI Grounded Language Model (GLM) Blog Post](https://contextual.ai/blog/introducing-grounded-language-model)
* [FACTS Benchmark for Evaluating Grounding in LLMs](https://github.com/ContextualAI/examples/blob/main/10-FACTS-benchmark)
* [Notebook and Examples](https://github.com/ContextualAI/examples/blob/main/03-standalone-api/02-generate/generate.ipynb)