AI can speed up looking things up. It runs from a quick answer to a multi-step deep research task that reads many sources and returns a written report with citations. Both are a first draft you verify before anyone acts on it.
The loop
Explain first, then confirm at the source
Ask for a plain explanationhow a rule usually works
Ask where it comes fromand to web-search current items
Confirm at the official sourcebefore you rely on it
A chatbot is a fast way to orient yourself, not a citation. Anything a client will act on gets confirmed against the official page.
A simple question gets a short answer. A deep research run reads across many sources over several steps and returns a written report with citations you can open.
ChatGPT, Gemini, Perplexity, and Claude each offer a deep-research or research mode; a general chatbot handles lighter lookups. Some, such as Gemini and ChatGPT, show you a research plan before they run, which is the best moment to redirect the scope.
Scope what documents a benefit or a process requires, so you walk into a meeting already knowing the checklist.
Compare eligibility rules across programs to see which one fits a situation, with an official source for each rule.
Find recent changes to a policy or a form so you are working from the current version.
For grant writing, a deep research run can do more than pull statistics; it can scan prior studies on the relationship between two factors, or on interventions that improved an outcome you care about, and summarize their length, design, and how they measured results, with a source and a link for each, which you then read before you cite it.
The tool can retrieve a weak source, misread a good one, or state a confident conclusion its sources do not support, so open every link and read the citation.
Rules change and the model has a training cutoff, so anything that determines what a client does gets confirmed against the official source, and client identifiers never go into a query.
How it works, and where it breaks
1Research mode searches first, then summarizes what it found
A research or deep-research mode adds a step in front of the writing. It runs searches, pulls back a set of documents, and then writes its answer from those documents while citing them. This design is called retrieval-augmented generation, and it was introduced to ground a model's output in retrieved text so it leans less on memory alone (Lewis and colleagues, 2020). Grounding in real sources is why a good research report can be more current and more checkable than a plain chatbot answer.
2Grounded does not mean correct
Retrieval improves the odds; it does not guarantee truth. The tool can retrieve a low-quality or outdated page, misread a source it did retrieve, or write a confident synthesis that its own citations do not actually support. Reviews of these systems document that generated text is often fluent and plausible while being unfaithful to its source or unsupported by fact (Ji and colleagues, 2023). On an independent benchmark of deep-research systems, even the most accurate cited a source that did not support its claim about one time in ten, and the weaker ones erred far more often (Du and colleagues, 2025); a separate audit of AI search tools found wrong citations in more than sixty percent of queries, and the tools seldom signaled any doubt (Jazwinska and Chandrasekar, 2025). The citation next to a sentence tells you where the tool looked; it does not confirm the sentence is right. Open each link and confirm it says what the report claims.
3A missing answer still comes back as an answer
When the searches turn up little, the tool rarely says nothing. It composes a plausible response from whatever it has, which is how a benefit amount or a filing rule that was never really found ends up stated with full confidence. The stakes are not hypothetical: by early 2026 a public database had logged more than a thousand court filings that contained AI-fabricated citations, which is why immigration and legal-aid guidance now tells staff to confirm every AI-supplied citation against the primary source (Charlotin, 2026). Ask it to give an official source and a link for every point and to flag anything it cannot confirm, then treat unflagged claims with the same caution.
Research you can check
How to run a research task you can trust
Name the question plainly, say what you need back, such as a document list or a comparison, and give exact names: the program, the form number, the state or county.
If the tool shows a research plan before it runs, read it and redirect the scope, since this is the cheapest moment to fix a wrong direction.
Ask it to use official or primary sources, to give a source and a link for every point, and to flag anything it cannot confirm.
Leave client names and identifiers out of the query.
Open each link and read the citation to confirm it says what the report claims.
Confirm anything a client will rely on against the official program or agency site before you use it.
A benefit or process you must explainA first-draft document list to verify
Scope the documents a process requires, then verify
I need to help someone apply for the benefit or process I name, in the state or county I name. List the documents and the steps it usually requires. Give an official source and a link for each point, and mark anything that varies by location or that you are not sure about. [name the program and the location, no client identifiers]
Guardrail. Open every source and confirm each document against the official program page before a client relies on it.
A form or rule you use oftenA checked list of what changed
Find recent changes to a form or rule
Tell me whether the form or rule I name has changed, what changed, and when. Give me the official source and a link for each change, and flag anything you cannot confirm. [name the form or rule and the location]
Guardrail. The model can be out of date, so confirm every change on the official site before you act on it.
Two or more programs a client might useA sourced eligibility comparison to verify
Compare eligibility rules across programs, with a source for each
Compare the eligibility rules for the programs I name, in the state or county I name. Lay it out as a table with one row per rule, one column per program, and an official source and link in each cell. Mark any rule that varies by location or that you cannot confirm. [name the programs and the location, no client identifiers]
Guardrail. Open every source and confirm each rule on the official program page before a client relies on it. See Module 2.
A topic for a grant narrativeA first-draft sourced literature scan
Scan prior reports and studies to seed a grant narrative
Find recent studies and reports on the topic I name that I could cite in a grant narrative, including any on the relationship between the two factors I name and on interventions that improved the outcome I name. For each, give the title, the author or organization, the year, a one-line summary of what it found, the length and design of any intervention, how it measured outcomes, and a link. Note which are peer reviewed and which are advocacy or government sources, and flag anything you cannot find a real link for. [name the topic and the population]
Guardrail. Open and read every source before you cite it, since the tool can list a title that does not exist or a link that does not match. See Module 1.
Worked example
When the citation does not back the claim
You ask a research mode which documents a client needs to apply for a benefit in your state, and you request an official source and link for each item.
The report is well organized and lists eight documents, each with a citation to what looks like the state agency site.
Opening the links, six point to the right page and match. One links to a neighboring state's rules, and one links to a real page that never mentions the document the report attached to it.
You keep the six confirmed items, correct the two the sources do not support, and check the final list against the official application page. The citation showed where the tool looked, and confirming each one is what made the list safe to use.
Guardrail. Treat every research result as unverified. Open each link, check each citation, and confirm anything a client relies on against the official source. Keep client identifiers out of every query. See Module 2 and Module 1. For turning what you find into a grant narrative, see Storytelling.
Sources
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Kuttler, H., Lewis, M., Yih, W., Rocktaschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems 33. https://arxiv.org/abs/2005.11401
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38. https://doi.org/10.1145/3571730
Du, M., Xu, B., Zhu, C., Wang, X., & Mao, Z. (2025). DeepResearch Bench: A comprehensive benchmark for deep research agents. arXiv. https://arxiv.org/abs/2506.11763