Grounding: Definition and Examples

Grounding (anchoring) is a technique that involves providing the AI model with factual data, documents, or concrete context so that its responses are anchored in reality rather than generated solely from its internal knowledge.

Full definition

Grounding, or anchoring in French, refers to the process of providing a language model with factual and verifiable information as a reference base for generating its responses. The goal is to reduce hallucinations—those plausible but false responses—by forcing the model to rely on concrete data rather than solely on its internal parameters.

In practice, grounding can take several forms: including document excerpts in the prompt, connecting the model to a database via RAG (Retrieval-Augmented Generation), providing real-time web search results, or injecting structured data such as tables or product sheets. Each approach aims for the same goal: giving the model a 'source of truth' it can rely on.

Grounding is particularly crucial in professional contexts where precision is non-negotiable: legal writing, financial analysis, technical customer support, or scientific research. Without anchoring, a model can invent statistics, cite non-existent sources, or erroneously mix information. With good grounding, the model becomes a reliable tool for summarization and analysis.

It is important to distinguish grounding from simply giving instructions. Grounding specifically concerns providing factual data as raw material, while instructions guide the behavior and format of the response. Both are complementary: a well-designed prompt combines solid anchoring with clear instructions to achieve results that are both accurate and well-structured.

Etymology

The term 'grounding' comes from English 'ground' (soil, anchoring). In linguistics and cognitive science, the concept of 'grounding' refers to the process by which symbols and words acquire meaning by being connected to concrete experiences of the real world. In AI, the term has been adopted to describe the anchoring of a model's responses in verifiable factual data, as opposed to purely statistical generation.

Concrete examples

Analysis of a financial report

Here is the 2025 annual report of Company X [inserted document]. Based SOLELY on the data from this report, summarize key financial performance and identify main trends.

Customer support grounded in documentation

You are a support agent for our software. Here is our knowledge base: [inserted FAQ and documentation]. Answer user questions by citing only information present in this documentation. If the answer is not there, state it clearly.

Journalistic writing based on sources

Here are three news articles on the pension reform: [inserted articles]. Write a factual summary attributing each piece of information to its source. Do not add any information that does not come from these articles.

Practical usage

To apply grounding effectively, always include source data directly in your prompt and explicitly ask the model to limit itself to it. Use formulations such as "based solely on the following document" or "cite your sources." For recurring use cases, consider a RAG architecture that automatically retrieves relevant documents before each query.

Related concepts

RAG (Retrieval-Augmented Generation)HallucinationContext WindowFew-Shot Prompting

FAQ

What is the difference between grounding and RAG?

RAG (Retrieval-Augmented Generation) is a specific technical method of grounding. Grounding is the general concept of anchoring responses in factual data, while RAG is an architecture that automates this process by dynamically retrieving relevant documents from a database before injecting them into the prompt. You can do grounding manually (by copy-pasting a document into the prompt), but RAG industrializes this approach.

Does grounding completely eliminate hallucinations?

No, grounding significantly reduces hallucinations but does not eliminate them completely. A model can still misinterpret the provided data, make incorrect inferences, or mix information from multiple sources. To maximize reliability, combine grounding with explicit instructions ("only answer if the information appears in the document") and human verification of critical responses.

How do I know if my prompt is sufficiently grounded?

A good test is to check whether each claim in the model's response can be traced back to a source data you provided. If the model produces information you cannot find in your context, it is a sign of insufficient grounding. Add more relevant data, tighten instructions to limit the model to provided sources, or ask it to explicitly cite its references for each point.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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