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Grounding

Intermediate
Search, Embeddings and RAG
Policy officers
Communications teams

Also called: grounded answer.

Detail

In five seconds

What it is

Grounding ties an AI answer to specific retrieved source passages so the reply is based on provided material rather than only on the model's general patterns.

In plain words

Grounding means giving the AI the exact source text it should use before it answers, so the reply is more connected to evidence.

An everyday example

Someone at a railway station asks which platform to use and whether a change is required. Rather than relying on memory, their companion reads the relevant lines on the departure board and the service notice, then answers from that information. The answer is tied to visible sources and can be checked. It may still be wrong if the wrong service was selected or the display is out of date, just as grounding depends on retrieving the right material.

Where the story stops working

A human librarian can deliberately judge credibility, notice subtle contradictions and explain uncertainty in richer ways. A model does not read with human judgement; it predicts text from patterns and can still misuse a passage, overstate what it says or combine sources badly. Also, real grounding often works on short text chunks and similarity scores, not whole books placed on a table.

Remember thisGrounding gives the model specific retrieved material to answer from and makes the result easier to verify. It does not guarantee accuracy if the source, retrieval or interpretation is wrong.

Understand it

Why it matters

For non-specialists, grounding matters because it changes what kind of answer you are getting. Without grounding, a model may produce fluent text that sounds plausible but is not tied to a source. With grounding and access to its sources, you can inspect where the answer came from, check whether the source is current, and notice when the source does not support the claim. That helps with policy work, communications, research and operations where you need traceability, not just polished wording. It also helps you ask better questions: 'What source was this based on?' and 'What was not found?' are often more useful than simply asking whether the answer is correct.

How grounding connects a question to source-based answers

After viewing this, the reader should be able to explain that grounding adds retrieved source passages to the model's input so the answer can be tied to evidence.

01
1. Ask
A user asks a question such as a policy or research query.
Show a speech bubble on the far left labelled 'Question' with one short sample query beneath it.
02
2. Search sources
The system searches an approved document collection for passages that appear relevant.
Show a stack of document cards above or behind a search box, with an arrow from Question to Documents labelled 'retrieve relevant passages'.
03
3. Select passages
A few top passages are chosen, often by semantic similarity and ranking.
Show 3 highlighted text snippets pulled out from the document stack, with small rank badges 1, 2, 3.
04
4. Build grounded prompt
The selected passages are added to the model input with instructions to use them.
Show a prompt panel combining the user's question and the highlighted snippets, with labels 'instructions', 'question', and 'sources'.
05
5. Generate answer
The model produces a reply that should stay close to the supplied passages and may include citations.
Show an answer box on the right with two short sentences and superscript citation markers linked visually back to the source snippets.

A left-to-right flow with five boxes: user question, document collection, retrieval, grounded prompt, and answer with source links. Read it as a process: the question triggers search over documents, selected passages are inserted into the prompt, and the model produces an answer based on that material.

Hovering or tapping each step highlights the arrows feeding into it and reveals one sentence on what could go wrong there, such as poor retrieval or weak source coverage.

Describe this without the diagram

The diagram shows a five-step process from left to right. First, a user asks a question. Second, the system searches an approved set of documents. Third, it selects a few relevant passages. Fourth, it combines the question, instructions and those passages into one prompt for the model. Fifth, the model generates an answer that is intended to rely on those passages and may show citations. The main lesson is that grounding happens by inserting retrieved source text into the model's input before answer generation.

With reduced motion: A static five-column flowchart replaces animation. Arrows connect Question to Documents, Documents to Selected Passages, Selected Passages to Grounded Prompt, and Grounded Prompt to Answer with Citations. Highlight colours indicate the passages carried forward.

Grounding means giving a model relevant source material before it answers, so the answer can be anchored in that material. The source material is often a few passages retrieved from documents, policies, reports or webpages. “Retrieved” means found by a search step, often by meaning rather than exact word match.

What goes in? Usually there is a user question, a collection of approved documents, and a retrieval step that selects the most relevant passages. Those passages are then added to the model prompt, which is the instruction and context sent to the model.

What happens? The system asks the model to answer using the retrieved passages. In a good design, the model is also asked to stay within those sources, quote or cite them, and say when the sources do not contain enough information. Grounding does not guarantee correctness, but it reduces the chance that the model will produce unsupported claims.

What comes out? Ideally, you get an answer that is connected to identifiable source text, often with citations. This is different from a citation alone: a citation may simply be attached after the fact, while grounding means the source passages were part of how the answer was produced.

Grounding is commonly used in retrieval-augmented generation, often shortened to RAG. It is closely related to retrieval, semantic search and ranking, but it is not the same as those steps. Retrieval finds candidate passages; grounding is the act of using those passages to constrain and support the answer.

How “Checking the departure board before answering” maps onto the real thing

In the story	In the AI system
student's question	user prompt
librarian pulling relevant books and pages	retrieval of relevant passages
answering while looking at the opened pages	grounded generation using supplied context
books on the table	source documents or knowledge base

A worked example

A reporting officer in a regional bureau is finalising a progress report before close of business and asks an internal assistant what remains before a publication can be cleared. The indexed corpus contains the current workplan and the latest clearance note.

Starting from: Question: What must happen before clearance is requested? Retrieved passages: (1) Workplan: “Complete the accessibility review before publication.” (2) Clearance note: “Front-office clearance may be requested once the accessibility review is recorded as complete.”

The system searches the approved indexed corpus for passages concerning clearance and outstanding publication steps.
It ranks the clearance note and the relevant workplan entry highest because both address the condition that must be met before clearance.
The passages are inserted into the prompt with an instruction to answer only from the retrieved material and identify the supporting passages.
The model generates a concise answer conditioned on the question and those passages.

Result: The accessibility review must be completed and recorded before front-office clearance is requested. The answer is supported by retrieved passages 1 and 2. The reporting officer can open both passages before updating the progress report.

In a United Nations workplace

At close of business, a humanitarian affairs officer in a country office is coding open-ended answers from a needs assessment for the next situation report. An approved assistant retrieves the relevant definitions and examples from the assessment’s coding guide before suggesting a code for each de-identified response.

The suggested code is grounded when the model receives the retrieved passages from the coding guide as context and bases its answer on them. Showing those passages lets the officer check whether the correct definition was retrieved and applied, rather than accepting a plausible label on trust.

Before you try thisNeeds assessment responses may contain personal, protection-sensitive or security-sensitive information. Raw responses must not be entered into a tool that is not approved for that material. Use only permitted, access-controlled data, remove identifiers where required, and retain human review because grounding can still retrieve or apply the wrong passage.

Where you may hear it

A vendor demo during procurement of an internal document-search tool
An ICT governance board reviewing a RAG assistant
A programme office testing questions against its monitoring reports
A chief of section asking why an answer has no supporting passage

Avoid confusion

Often confused with

Common misconceptions

Common misconception: If an answer is grounded, it must be true.
Correction: Grounding improves traceability, not certainty. The retrieved passage may be outdated, incomplete, low quality or misread by the model. A grounded answer can still be wrong, but you have a better chance to inspect and correct it because the source is visible.
Common misconception: Grounding and citation mean the same thing.
Correction: They are related but different. A citation is a reference shown to the user. Grounding is about the generation process: the source passages are provided to the model before it answers. A system can show citations without truly using the source, and a grounded system can technically be built even if citations are not displayed.
Common misconception: Grounding only matters for facts on the public web.
Correction: It is also useful for private or organisational knowledge bases, such as manuals, resolutions, procedures or approved reports. In those settings, grounding helps the model rely on the right internal text rather than generic training patterns, though data governance still matters.

Quick check

Which statement best describes grounding in an AI question-answering system?

Go deeper

The simple version

Grounding means giving the AI the exact source text it should use before it answers, so the reply is more connected to evidence.

The technical version

In a typical pipeline, a query is used to retrieve relevant document chunks from an indexed corpus, often through semantic search, keyword search or a hybrid method. The top-ranked chunks are inserted into the model prompt as context, along with instructions such as answering only from the provided material or citing it. The language model then generates a response conditioned on both the user query and the retrieved context. This is why grounding is often discussed inside retrieval-augmented generation. It is distinct from retrieval itself, which finds candidate passages, and distinct from citation display, which may or may not reflect the actual conditioning context. Grounding usually works better when chunking, indexing and ranking are well designed, but it still does not ensure factual correctness, faithful use of sources or complete coverage of the answer.

Reviewed external resources

EncyclopaediaWikipedia search: Grounding (opens in a new tab)Wikipedia
This Wikipedia search results page helps a beginner see that the word 'grounding' has several meanings, so it suits readers who first need orientation rather than a single authoritative article.
search results, not one pagelink checked 2026-07-31
CourseEmbeddings (opens in a new tab)Google
This Google course page on embeddings suits readers who want the mechanism behind meaning-based retrieval that often supports grounding.
Intermediatelink checked 2026-07-31
InteractiveEmbedding Projector (opens in a new tab)TensorFlow
This TensorFlow Embedding Projector interactive suits visual learners who want to explore how similar items can be positioned near each other in vector space.
Starterlink checked 2026-07-31
VideoVideo explanations: Grounding (opens in a new tab)YouTube search
This YouTube search results page suits readers who prefer video explanations and want to compare different presenters' ways of explaining grounding.
search results, not one pagelink checked 2026-07-31
DiscussionCommunity discussion: Grounding (opens in a new tab)Reddit search
This Reddit search results page suits readers who want informal community questions and examples, but it is best used critically because discussion quality varies.
search results, not one pagelink checked 2026-07-31

Where this came from

Drafted with a language model through an offline pipeline, checked against this site's schema, and every external link above fetched to see whether it answers. Treat it as a starting point and check anything that matters against the primary sources. How this site is built and what that means.

The one thing to remember

Grounding gives the model specific retrieved material to answer from and makes the result easier to verify. It does not guarantee accuracy if the source, retrieval or interpretation is wrong.