Concern 104 · Working draft · editorial review in progress

RAG grounding failure

RAG grounding failure happens when an AI answer does not faithfully use the documents retrieved to support it.

Understanding the concern

The system may ignore a key condition, combine conflicting passages, or state something its citations do not establish. Fetching a good source is therefore only one part of producing a grounded answer.

This differs from retrieval-index poisoning: the retrieved material may be accurate and untampered with. The failure is the connection between that material and the answer. Review should compare each important claim with the evidence, not merely count citations.

An example

A policy says an exception applies only to full-time staff, but the summary presents it as available to everyone.

Illustrative unless identified as a documented case.

Words in plain English

AI
Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
RAG
Retrieval-augmented generation: an AI looks up material before writing an answer. Finding a document does not guarantee that the document or the answer is correct.
Data poisoning
Deliberately changing training material to push a system toward unwanted behavior.

Evidence and its limits

NIST supports grounded evaluation; PoisonedRAG is contrasting retrieval-system context, not proof that every grounding error is an attack.

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Broader frameworks and background

These resources provide context. They do not establish every claim in this concern.

A question to discuss

Does every important claim follow from the cited passage, including its conditions and exceptions?

Keep track of different views, what needs checking, and any next step in your own discussion notes.

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Principles for the conversation

Before you finish · reflect together

Virtues and principles to consider

Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.

Humility

Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.

State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.

Ask together: What might we be missing, and who could help us see it?

Prudence

Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.

Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.

Ask together: What is a careful next step, and when should we stop or change course?

Truthfulness

Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.

Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.

Ask together: What can we honestly say, and what still needs checking?

Editorial ratings

These are editorial judgments from the register, not measured probabilities or predictions about your situation.

Severity
Serious · 3/5
Likelihood
Documented at scale · 5/5
Time horizon
Now
Register risk score
15
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