# Grounding and Citations > Grounding ties an AI answer to specific source material rather than the model's general knowledge. Citations show where each claim came from. In construction, that might mean pointing to Section 09 29 00, paragraph 3.4, for a level 5 finish requirement. - Stages: Estimating, Preconstruction, Closeout - Concepts: Retrieval (RAG), Evaluation - Published: 2026-08-28 - Canonical: https://buildr.com/library/grounding-and-citations ## Why it matters in construction Construction documents are contractual. If an estimator carries a scope item because an AI said the spec required it, and the spec did not, that is real money. If a project engineer tells an owner the warranty on the RTUs is five years because the model said so, and the O&M manual says one, that is a warranty claim the GC eats. Grounding limits the model to the documents provided for the task. Citations make its work checkable. Together, they give an estimator a way to review an answer before relying on it. ## How it works 1. **Retrieve the relevant source.** Before the model answers, the system pulls the passages most likely to contain the answer from the spec, the RFP, the sub proposals, or the closeout package. This is the retrieval step in RAG. 2. **Instruct the model to answer only from those passages.** The prompt tells the model that if the answer is not in the provided material, it should say so rather than guess. 3. **Attach references.** For each factual claim, the output identifies the document and location, and may quote the source text. 4. **Link to the citation.** The interface should let the reader open the relevant page or section. The citation needs enough detail to be useful. "The spec" is not enough on a 400-page book; a section and paragraph let an estimator find the relevant text. The system also needs to handle gaps honestly. "Not found in documents" is a valid answer when the source material does not contain the answer. Grounding does not eliminate errors. A model can still misread a passage, but a reviewer can see the source and catch the mistake. ## Example in practice Let's say a commercial GC is bidding a $31M K-12 addition. The estimator asks the firm's AI plan and spec tool whether the project requires seismic bracing on suspended ceilings. The tool answers yes, cites Section 09 51 13, paragraph 2.6, and quotes the two sentences requiring bracing per the referenced ASCE standard. It also notes that Structural sheet S-001 general note 14 references the same requirement. The estimator opens both references, confirms the requirement, and adds bracing to the ACT scope in the bid package. When three subs exclude it in their proposals, the team can point to the cited requirement and resolve the gap before award. ## Go deeper - [AI for Estimators: 5 Practical Uses that Work Today (Not Someday)](/blog/ai-for-construction-estimators.md) - [AI and Construction: The GC's No BS Guide to What Works in 2026](/blog/ai-construction-gc-guide-2026.md) ## How Buildr applies this Every line item and scope gap Buildr extracts from a sub proposal links back to the source text it came from. See [Buildr Estimating](/estimating). ## Related terms - [Hallucination](/library/hallucination.md): A hallucination occurs when an AI language model states something confidently that is untrue or unsupported by the source. For example, it might invent a unit price or an exclusion that does not appear in a subcontractor's proposal. - [Retrieval-Augmented Generation (RAG)](/library/retrieval-augmented-generation.md): Retrieval-Augmented Generation (RAG) finds relevant pages in a company's own documents and gives them to an AI language model before it answers. It grounds a construction team's responses in its actual specs, proposals, and project history instead of the model's general knowledge. - [AI Plan and Spec Review](/library/ai-plan-and-spec-review.md): AI plan and spec review uses language models to compare a construction drawing set with its specification book. It flags conflicts, missing information, and risk items for the preconstruction team to review before pricing. - [AI RFP Analysis](/library/ai-rfp-analysis.md): AI RFP analysis uses language models to read a construction request for proposals, extract the requirements, deadlines, evaluation criteria, and risk terms, and summarize them so a general contractor can decide whether and how to pursue the job. - [Human-in-the-Loop](/library/human-in-the-loop.md): Human-in-the-loop is an AI design pattern in which a person reviews, corrects, or approves the model's output before it takes effect. In construction, an estimator still owns the award and a BD lead still approves a pipeline update, while the AI handles reading and drafting. ## Referenced by - [AI Evals](/library/ai-evals.md): AI evals are structured tests that measure how well an AI system performs on a defined task using examples with known answers. A construction firm can use them to test whether a tool reads its sub proposals or RFPs accurately before relying on it on bid day. - [AI Proposal Generation](/library/ai-proposal-generation.md): AI proposal generation uses language models to draft construction proposal content, such as approach narratives, past project descriptions, and team bios, from a general contractor's own history and the RFP requirements. The pursuit team then reviews and edits the draft. - [Natural Language Querying](/library/natural-language-querying.md): Natural language querying lets a construction team ask questions about business data in plain English, such as 'what is our weighted pipeline for Q4' or 'which superintendents are free in March,' and get answers from the CRM, backlog, and workforce plan without building reports or formulas. - [Prompt Injection](/library/prompt-injection.md): Prompt injection is an attack in which instructions hidden in content an AI model reads, such as a subcontractor proposal or RFP attachment, alter the model's behavior. It is a major security risk for construction AI tools that process outside documents. - [Scope Gap Detection](/library/scope-gap-detection.md): Scope gap detection uses language models to compare subcontractor proposals with a construction bid package. It flags work that no sub has priced, that a sub has excluded, or that overlaps between trades before the general contractor commits to a number. ## FAQ ### Does a citation guarantee the answer is correct? No. A model can cite a passage and still misread it. A citation narrows the review to the relevant passage instead of the whole document. Treat it as a review aid, not proof. ### What should a good citation look like in a construction tool? It should include the document name, page or section number, and quoted passage. Ideally, a link opens the source at that location. A document name alone is not enough for a 400-page spec book. ### Can the AI answer without a source? A well-designed grounded system should say it could not find the answer in the provided documents rather than fill the gap from general knowledge. If a tool never says 'not found,' be suspicious.