What is Grounding?
Grounding is the system practice of supplying a generative AI model with bounded external evidence at runtime and requiring material output claims or actions to remain traceable to that evidence, observed state, or Tool results.
How It Works
This entry uses Grounding in the operational generative-AI sense. It is related to but different from the Symbol Grounding problem in cognitive science, Visual Grounding that maps language to image regions, and sensorimotor grounding in robotics.
Evidence Grounding starts before generation: each document passage, database record, search result, API response, user file, or sensor observation needs a stable identity, immutable revision or Content Hash, precise Locator, observation time, validity interval, authority policy, and access scope. Retrieval then has to find evidence that is relevant and sufficient for the question. Retrieved content is not automatically true, current, complete, mutually consistent, authorized, or safe; it can also carry Indirect Prompt Injection. Applications should filter permissions before retrieval, separate instructions from untrusted evidence, preserve Provenance through reranking and Context assembly, and pass Tool errors, partial results, and timestamps explicitly.
During and after generation, material statements should be decomposed into Atomic Claims and mapped to the evidence that entails, contradicts, or cannot support them. Answer Faithfulness asks whether claims follow from the supplied context, while Factual Correctness asks whether they agree with an external reference or the world. A model can faithfully repeat a stale or incorrect source, and it can state a correct fact from Parametric Memory without being grounded in the evidence provided for that run.
Citation is another separate layer: a Citation Pointer must resolve to the claimed source and span, the span must support the exact claim, and citations must cover all material claims. A citation added after generation does not prove that the source influenced the answer. RAG is one Grounding architecture, but Search, databases, Knowledge Graphs, typed Tool calls, and observations can also supply evidence.
A System Prompt that says to use only the sources is a best-effort instruction, not an enforcement boundary. When evidence is missing, stale, unauthorized, or contradictory, the system should return a bounded partial answer, abstain, ask for clarification, or escalate according to policy.
Production evaluation must separately measure corpus authority and freshness, permission leakage, retrieval recall and sufficiency, claim support and contradiction, citation precision and coverage, external correctness, answer relevance and completeness, abstention behavior, Prompt Injection resistance, latency, cost, and end-state task success. Automated judges require versioning and calibration against human-labeled failure cases.
Key Characteristics
- Binds material Claims to runtime Evidence or observed state rather than relying only on Parametric Memory
- Preserves source identity, revision, hash, locator, timestamp, authority, validity, and access scope through the response
- Separates retrieval relevance and sufficiency from Answer Faithfulness, Citation Quality, and external Factual Correctness
- Represents support as a Claim-Evidence relation such as entailed, contradicted, or insufficient instead of treating source presence as proof
- Requires explicit abstention or escalation when evidence is missing, stale, conflicting, unauthorized, or returned by a failed Tool
- Treats external content as untrusted input and keeps authorization, write controls, and Judge calibration outside the model
Common Use Cases
- Generating RAG answers whose material claims link to exact, revisioned source passages
- Answering account, inventory, or status questions from typed Tool results with explicit success and observation time
- Producing research or policy summaries with source-authority, freshness, conflict, and citation checks
- Supporting regulated assistants with permission-filtered evidence, abstention, escalation, and auditable provenance
- Evaluating agents by comparing retrieved evidence, generated claims, citations, actions, and final environment state
Example
Loading code...Frequently Asked Questions
How is Grounding different from RAG?
Grounding is the broader evidence contract: material claims or actions must be traceable to authorized, current, and inspectable evidence. RAG is one architecture that retrieves documents before generation. Database queries, web search, Knowledge Graphs, typed Tool calls, and sensor observations can also ground a system, while a RAG pipeline can still be poorly grounded if retrieval is incomplete or the generator ignores its context.
How do Grounding, Answer Faithfulness, Factual Correctness, and Citation differ?
Grounding covers evidence acquisition, provenance, use, and verification across the system. Answer Faithfulness asks whether each claim follows from the supplied context. Factual Correctness compares the claim with an external reference or real-world state. A Citation is a pointer to evidence; it must resolve, support the exact claim, and cover material claims before it contributes evidence of Grounding.
Do citations guarantee that an AI answer is grounded?
No. A citation can point to a missing page, an irrelevant passage, a source that contradicts the claim, or a document attached only after generation. Validate Citation Locator resolution, Claim-Evidence entailment, and coverage separately. Also verify source authority, revision, freshness, and permissions because a faithfully cited source can still be wrong or stale.
What should a grounded system do when evidence is missing or conflicting?
It should follow a declared policy: return only the supported portion, abstain, request missing information, show the conflict, or escalate to a qualified reviewer. It should not silently fill gaps from model memory. The result should preserve the Evidence IDs, conflict status, timestamps, and reason for the terminal decision.
How should Grounding be evaluated and secured?
Use labeled slices for answerable, unanswerable, stale, conflicting, deleted, and access-denied cases. Measure source quality, retrieval coverage, Claim support and contradiction, Citation precision and coverage, external correctness, completeness, abstention, and end-task success separately. Calibrate automated judges against human labels, treat retrieved content as untrusted, and enforce permissions and write authorization in code.