The committee may need five minutes to make an ARC decision. The manager can spend an hour getting the request ready for that decision.
A homeowner emails a fence application, survey, contractor quote, product sheet, and several photos. Before anyone can say yes or no, someone has to piece together what is being proposed, determine whether the submission is complete, find the applicable standards, and prepare something the reviewer can act on.
The prompt in this guide handles that desk work. It reviews the submission, finds missing information, compares the proposal with the community’s ARC guide, and shows the source behind every finding. AI does not replace the reviewer, but it makes the request much easier to review.
[Give AI the same files a manager would use]
AI begins with no knowledge of the community. It does not know which fence guide applies, which application is current, or whether the association requires a marked survey.
For many communities, the source material is as simple as the current ARC guidelines in PDF form.
This is a good opportunity to update your ARC guidelines if they are out of date. More documents are not always better. Uploading years of amendments, old forms, and unrelated policies can introduce conflicting language that has nothing to do with the request. Start with the documents you would normally place in front of the reviewer.
Open a new conversation in ChatGPT or Claude and attach the ARC guidelines, along with the homeowner’s individual request submission.
[The prompt makes AI stop before it guesses]
Most weak AI results begin with an overly broad question:
Weak prompt
Can this fence be approved?
That invites AI to jump directly to an answer, even when the submission is incomplete or the guide does not address the issue.
The ARC Request Resolver follows a different sequence.
First, it checks which documents it received. If the ARC guide is unreadable, two different communities appear in the files, or multiple versions of the same guide conflict, it stops and asks the manager to correct the source material.
Next, it checks whether the homeowner supplied what the application requires. If something is missing, it does not treat the omission as a violation or continue to analyze the project. It prepares one consolidated email requesting everything needed.
When the homeowner replies, the manager adds the new information to the same conversation and tells the AI to continue. Only then does it compare the completed request with the ARC guide.
This sequence prevents one of the most common AI mistakes: replacing missing information with a plausible assumption.
[The ARC Request Resolver prompt]
This is the full prompt, ready to paste into ChatGPT or Claude alongside the community’s documents.
[Get the complete ARC Request Resolver prompt]
The prompt checks the submission, requests missing information, performs a citation-backed review, prepares a proposed resolution, pauses for the human decision, and drafts the homeowner response. No prompt writing or technical setup is required.
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[What the manager gets back]
Attach a complete fence request and the prompt goes to work. Instead of a bare yes or no, it hands back a review the manager can actually check. Every detail of the proposal is lined up against the community’s own guide, with the section, page, and exact quotation behind each finding:
Contractor quote, p. 1
Fence Standards §3.1, p. 7: “Rear-yard fences may not exceed six feet.”
Appears consistent
Product sheet, p. 2
Fence Standards §3.2, p. 7: “Approved materials include white vinyl.”
Appears consistent
Marked survey, p. 1
Fence Standards §3.4, p. 8: “Fences must begin at least ten feet behind the front corner.”
Potential conflict
This is a fictional example. The prompt uses the exact language from the community’s actual guide.
That table is the point of the whole exercise. Requiring a section, page number, and quotation is not about producing a more formal-looking answer. It lets the manager see how the AI reached its conclusion. Without that evidence, a confident AI answer is hard to tell apart from a confident guess. With it, the manager opens the cited page and verifies the result in seconds.
The standard is simple: no quote, no conclusion. If the guide does not address white vinyl fences, the AI says the guide is silent. It does not borrow a rule from another association or turn a general preference into a requirement. And if the homeowner left something out, a fence line that was never drawn on the survey, the AI asks for it rather than guessing where the fence will go.
From there, the prompt assembles a short packet for the authorized reviewer: what the homeowner wants, what appears consistent, what may conflict, and a proposed resolution clearly labeled as an AI recommendation. The reviewer decides. The AI does not.
Once that decision is recorded, the AI drafts the homeowner response, citing the same guide language and adding nothing the reviewer did not say. Say the homeowner had asked for a seven-foot fence, a foot over the limit, and the reviewer denied it. The draft comes back ready to send:
Drafted homeowner response
Subject: ARC Decision on Your Fence Application, 14 Maple Court
Dear Mr. Reyes,
Thank you for your application to install a fence at 14 Maple Court. The Architectural Review Committee has reviewed the request and is unable to approve it as submitted.
The application proposes a seven-foot fence. The community’s Fence Standards limit rear-yard fence height: “Rear-yard fences may not exceed six feet” (§3.1, p. 7). The proposed height exceeds that limit.
You are welcome to resubmit a revised application for a fence of six feet or less. If you have any questions, just reply to this email and we will be glad to help.
Sincerely, Oakridge Community Association
Every line of that email traces back to the reviewer’s decision and the guide behind it. Nothing was added to make the denial sound more official than it is.
[Where the manual workflow breaks]
The prompt can reduce an hour of document work to minutes. But it is still a manual workflow.
For every request:
Open the AI tool → Start or locate the correct conversation → Find the correct community’s current ARC guide → Upload the application and every attachment → Add each subsequent homeowner reply → Keep documents from different communities separate → Copy the review to the decision-maker → Return with the recorded decision → Review and send the final response
That may work well for one request. It becomes harder when a portfolio manager is juggling multiple communities with different applications, fence rules, paint standards, and submission requirements.
A general AI tool does not know which manager-approved guide belongs to which community unless the manager supplies it. AI reduced the analysis, but the manager is still moving every piece by hand and initiating every step.
[Make the human review the only manual step]
Garfield removes that setup.
A homeowner can email a dedicated ARC inbox, or the manager can forward an existing request. Garfield connects the request to the manager-approved document set already assigned to that community, keeps the email and attachments together, and runs the completeness and cited review automatically.
If information is missing, Garfield prepares the consolidated homeowner email. When the reply arrives, it stays with the same request and the review continues.
Once complete, Garfield presents the authorized reviewer with:
The project summary
The submitted evidence
Any missing or unclear information
Potential conflicts
The exact section, page, and quotation behind every finding
A proposed resolution for human review
For a routine request, the remaining human action can be as simple as approving or rejecting the prepared recommendation.
Across a full book of communities, using AI to take a first pass is what lets a firm take on more communities without increasing burnout. A general AI prompt helps a manager complete the workflow faster. Garfield runs the workflow automatically and brings the manager or decision-maker in where human judgment is required.