Definition
Grounding connects a model's output to evidence supplied during the research task rather than relying only on what the model learned during training. Retrieved documents can provide that context. In company research, the important question is whether the generated answer follows from the correct source about the correct company, within the relevant date and scope.
A grounded workflow can still produce unsupported conclusions. Retrieval might return the wrong entity, a quotation might be misread, or a summary might broaden a narrow statement. A citation shows a claimed relationship to a source; it does not independently establish that relationship. Grounding therefore needs claim-level review, explicit missing states, and a way to inspect the retained evidence used for the answer.
In origination work
Ask narrowly defined questions with typed answers and evidence requirements. Compare the actual supporting passage with the recorded value, including limitations and negation. Docket separates Scout's collection from Audit, which rereads retained material without new fetches and strikes unquotable findings. That separation supports reviewing the supplied basis rather than accepting fluent prose as proof. Where evidence is incomplete, the useful result is a visible gap and a specific follow-up question, not a plausible completion.
What grounding means for a language model
A grounded answer is one whose claims are supported by supplied source material, not by what the model remembers from training. The standard technique is retrieval-augmented generation, introduced in a 2020 paper: the system retrieves relevant passages and the model writes its answer conditioned on them. Grounding is the property that the answer can be traced to those passages, and a reviewer can check that each statement is supported.
Retrieval alone does not guarantee grounding. A model can ignore the retrieved text, blend it with memory, or misread it. Research on attribution defines the question precisely: is a generated statement attributable to a specified source? Benchmarks now measure the extent to which model outputs with citations are actually supported by the cited passages, and the results show it is a property that must be tested, not assumed.
Failure modes that matter in research
A study of generative search engines evaluated whether statements and their citations agreed, and found that a substantial fraction of statements were not fully supported by what they cited. Other work shows that models use information in long inputs unevenly, with relevant material in the middle of a long context used less reliably than material at the start or end. Both findings affect how a research agent should be designed.
Practical consequences: keep the context short and relevant, ask for the supporting quotation with each answer, and verify that the quotation exists in the retained source. A grounded system accepts "not found" as an answer. An agent that always produces a value, even when the sources are silent, is optimising for completeness over truth.
Measuring grounding in practice
Evaluation frameworks for retrieval-based systems define measures such as faithfulness (does the answer follow from the retrieved context), answer relevance and context relevance. They can be automated using a model as a judge, with its own limits, or checked by people on a sample. The important property for acquisition research is that every extracted value has a quotation that a script can find verbatim in the stored source.
Combine mechanical and human checks. A verbatim-quote check catches fabricated or paraphrased support. A human sample catches the cases where a quotation exists but does not mean what the answer claims. Report both rates with sample sizes. A team that can state how often its system is grounded, and what it does when it is not, is making an honest claim.
Related terms
- Hallucination — Generated content that adds unsupported information or contradicts the source material it is meant to represent.
- Retained source — A saved version of source material preserved as the basis for a dated research finding.
- Evidence trail — A reviewable chain connecting a research conclusion to its supporting material, interpretation, and decisions.
Further reading
- Test the relationship between answer and evidence
- How the research agents work
- Hallucination
- Retained source
- Provenance
- Article: benchmarking AI agents for acquisition research
- Article: CRM, research database and evidence store
Sources and further reading
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, arXiv (2020). The paper that named the pattern of grounding a language model's output in retrieved documents.
- Measuring Attribution in Natural Language Generation Models, arXiv (2021). Defines Attributable to Identified Sources (AIS): whether a statement is actually supported by the document cited for it.
- Enabling Large Language Models to Generate Text with Citations, arXiv (2023). A benchmark for whether model-written statements are actually supported by the passages they cite.
- Evaluating Verifiability in Generative Search Engines, arXiv (2023). Human evaluation finding that a large share of citations in generative search output do not support the sentence they are attached to.
- Lost in the Middle: How Language Models Use Long Contexts, arXiv (2023). Retrieval accuracy degrades for material in the middle of a long context. Relevant to anyone dumping a whole website into a prompt.
- RAGAS: Automated Evaluation of Retrieval Augmented Generation, arXiv (2023). Reference-free metrics for faithfulness and answer relevance in retrieval pipelines.
To apply these distinctions to your own acquisition criteria, discuss your research brief or inspect the demo platform.