```yaml
product: AlterLab
title: "Grounding (AI) — Web Scraping Glossary | AlterLab"
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
source_url: https://alterlab.io/glossary/grounding
```

ai-agent

# Grounding (AI)

Grounding connects an AI model's output to verifiable, real-world data sources, reducing hallucinations by anchoring generated text in retrieved facts.

A language model that generates text purely from its parametric memory can produce confident but incorrect statements — hallucinations. Grounding addresses this by tethering generation to external, verifiable sources: retrieved documents, structured database records, or live web content. The model is instructed to base its response on the provided sources and to cite them, allowing the user to verify the output independently.

Grounding is the goal that RAG achieves technically: the retrieval step provides the ground-truth documents, and the model generates within the constraints of those documents rather than from free imagination. Grounding quality is measured by the degree to which generated claims are supported by cited sources.

For web scraping platforms, grounding is a key value proposition: AI agents equipped with live web scraping can ground their responses in current web data — today's prices, current news, live product availability — rather than training-time snapshots. This makes AI outputs trustworthy for time-sensitive queries.

### What is Grounding (AI)?

Grounding connects an AI model's output to verifiable, real-world data sources, reducing hallucinations by anchoring generated text in retrieved facts.

### How does AlterLab handle Grounding (AI)?

For web scraping platforms, grounding is a key value proposition: AI agents equipped with live web scraping can ground their responses in current web data — today's prices, current news, live product availability — rather than training-time snapshots. This makes AI outputs trustworthy for time-sensitive queries.

## Related Terms

[RAG (Retrieval-Augmented Generation)RAG is an AI architecture that grounds LLM responses in retrieved documents, combining a vector search step with language model generation to reduce hallucinations.](https://alterlab.io/glossary/rag)[LLM (Large Language Model)A Large Language Model is a neural network trained on vast text corpora that can generate, summarise, translate, and reason over natural language at human level.](https://alterlab.io/glossary/llm)[Tool UseTool use is the ability of an AI model to call external functions or APIs during generation, enabling it to retrieve live data, execute code, and interact with the world.](https://alterlab.io/glossary/tool-use)[EmbeddingsEmbeddings are dense vector representations of text (or other data) produced by a neural network, where semantically similar content maps to nearby points in vector space.](https://alterlab.io/glossary/embeddings)[MCP (Model Context Protocol)MCP is an open protocol by Anthropic that standardises how AI models connect to external tools, APIs, and data sources through a uniform server-client interface.](https://alterlab.io/glossary/mcp)

## Extract Grounding (AI) data from any website

AlterLab returns clean, structured data from any public URL — no scraper infrastructure needed. Start free, no credit card required.

[View API docs](https://alterlab.io/docs/api/rest)

[Back to Glossary](https://alterlab.io/glossary)