ChatGPT for Policy Summaries: Prompts That Work

Reading an insurance policy is nobody’s idea of a good time. A standard homeowners policy runs 40–60 pages. A commercial general liability policy can hit 100. Most clients don’t read them at all, which means they find out about exclusions at the worst possible moment — right after filing a claim.

Summarizing policies used to mean manually pulling out declarations pages, cross-referencing endorsements, and translating legal language into plain English. It took time. When you’re renewing 30 accounts in a week, you don’t always have that time. I started using ChatGPT for policy summaries about 18 months ago. It doesn’t replace judgment, but it accelerates the mechanical parts of the job substantially.

Here’s what I’ve found actually works — eight prompts I use regularly, with context on when each one is appropriate and where each one falls short.

Before You Start: What ChatGPT Can and Can’t Do Here

ChatGPT (GPT-4o) can read pasted policy text and extract specific information. It cannot access insurer portals, pull documents from URLs, or read PDFs directly unless you’re using a plugin or a platform that converts PDFs first. For most of these prompts, you’ll paste the relevant sections of the policy directly into the chat.

One consistent limitation: ChatGPT is confident even when it’s wrong. It will occasionally summarize a coverage limit incorrectly or miss a sublimit buried in an endorsement. Always verify the output against the actual document before sharing with a client. These prompts are a first pass, not a finished product.

Prompt 1: Quick Coverage Overview

When to use it: Client wants to know what they have before a renewal call. You need a one-page summary fast.

The prompt:

Below is the declarations page and coverage summary from a personal auto insurance policy. Please summarize the key coverages in plain English. For each coverage, list: the coverage type, the limit, the deductible (if applicable), and one sentence explaining what it covers. Format as a table.

[Paste declarations page text here]

Example output (paraphrased): ChatGPT returned a clean 6-row table with coverage types down the left column and limits, deductibles, and descriptions across the columns. It correctly identified the BI/PD split limits and noted the uninsured motorist coverage. It missed a roadside assistance endorsement that appeared on a separate page I hadn’t included.

Tip: Include all pages of the declarations, not just the first one. Endorsements often appear on later pages and will be omitted if you don’t include them.

Prompt 2: Plain-English Exclusions List

When to use it: Client is asking “what won’t this cover?” before a major purchase or event.

The prompt:

Below is the exclusions section from a homeowners insurance policy. List every exclusion in plain English, one per line. For each exclusion, note whether it is a standard exclusion common to most homeowners policies or appears to be unusual for this policy type. Do not interpret or editorialize — just list what the policy excludes.

[Paste exclusions section here]

Example output (paraphrased): The model produced a 14-item list. It correctly flagged flood and earthquake as standard exclusions and correctly identified a “mold remediation cap” sublimit of $5,000 as worth noting. It incorrectly classified a “fungi and bacteria” exclusion as unusual when it’s actually standard in most ISO HO-3 forms post-2000.

Tip: Always add context about the policy type and state in your prompt. “This is an ISO HO-3 policy in Texas” helps the model calibrate what’s standard vs. unusual for that form and jurisdiction.

Prompt 3: Comparing Two Quotes Side by Side

When to use it: Client is choosing between two carriers. You want to show the differences without building a spreadsheet from scratch.

The prompt:

I have two homeowners insurance quotes. I'll paste the key coverage details for each. Compare them in a table. Highlight in bold any coverage where the policies differ significantly (more than 10% difference in limits, or different deductible structures). Note at the bottom which policy appears stronger for each of these three scenarios: (1) roof damage from hail, (2) water damage from a burst pipe, (3) theft of personal property.

Quote A:
[Paste coverage details]

Quote B:
[Paste coverage details]

Example output (paraphrased): The comparison table was accurate for the items I included. The scenario analysis at the bottom was useful as a starting point — it correctly identified that Quote B had a better personal property limit but didn’t catch that Quote A used replacement cost while Quote B used ACV for personal property, because I hadn’t included that in my paste. Garbage in, garbage out.

Tip: Be explicit about what you’re including. If you want it to compare personal property valuation methods, you need to include that in what you paste.

Prompt 4: Commercial Policy Coverage Gap Analysis

When to use it: Small business client wants to know what exposures aren’t covered by their current BOP.

The prompt:

Below is a summary of coverages from a Business Owners Policy (BOP) for a [type of business — e.g., small retail clothing store with 3 employees, $800K annual revenue]. List the top 5 coverage gaps a business of this type typically has with a standard BOP. For each gap, explain: (1) what the exposure is, (2) whether the current policy covers it, (3) what additional coverage would address it.

[Paste BOP coverage summary here]

Example output (paraphrased): The model identified cyber liability, employment practices liability, and business auto as gaps — all accurate for a retail BOP. It also suggested hired/non-owned auto, which was worth discussing. One hallucinated item: it suggested the policy likely lacked “product recall coverage,” which is not a standard BOP gap for a clothing retailer and would only matter for a manufacturer. Flag it, but verify.

Tip: The more specific your business description, the better. Include SIC code if you have it. Vague descriptions produce generic lists.

Prompt 5: Client-Facing Policy Summary Email

When to use it: You want to send the client a readable summary of what they just bought. Not legal language — clear English.

The prompt:

Below are the key coverages from a personal auto insurance policy I just placed for a client. Write a short email (under 300 words) summarizing what they're covered for. Write in second person ("Your policy covers..."). Avoid insurance jargon. Do not include pricing. End with two specific things they should know about filing a claim if they need to.

[Paste coverage summary here]

Example output (paraphrased): Solid first draft. The model stayed under 300 words, avoided jargon, and included two useful claim tips (take photos at the scene, report to the carrier within 24 hours). It used the phrase “peace of mind” once, which I always delete — it’s meaningless filler.

Tip: Review the output for any coverage-specific claims. The model will sometimes state specific limits in the email body that are slightly wrong if the declarations page wasn’t formatted clearly. Double-check all numbers.

Prompt 6: Life Insurance Policy Summary for Beneficiaries

When to use it: Helping a beneficiary understand a policy after the insured has died — a conversation that needs clarity and care.

The prompt:

Below is a summary of a life insurance policy. The insured has passed away and I am helping the beneficiary understand what benefits they may be entitled to. Please explain in plain language: (1) the death benefit amount, (2) whether there are any exclusions that might affect payout, (3) what documentation the beneficiary will need to file a claim, (4) the typical timeframe for payout after a claim is filed. Write with care — this is for a grieving family member.

[Paste policy summary here]

Example output (paraphrased): The tone was appropriate. The model correctly explained contestability periods and noted suicide exclusions are typically only in force for the first two years. It suggested standard claim documentation (death certificate, completed claim form, policy number). Do not trust the timeframe estimate — it will give a range, and actual payout timelines vary significantly by carrier and claim complexity.

Tip: This is one case where you want to review the output carefully before sharing. Anything that touches a beneficiary expectation needs to be accurate.

Prompt 7: Health Insurance Explanation of Benefits Decoder

When to use it: Client got an EOB and has no idea what it means. They think they owe money they might not owe.

The prompt:

Below is an Explanation of Benefits (EOB) from a health insurance claim. Explain in plain language: (1) what service was billed, (2) what the insurance paid, (3) what the patient owes and why, (4) whether any amount was applied to a deductible, (5) whether the patient has any right to appeal. Use a numbered list. Assume the reader has no insurance background.

[Paste EOB text here]

Example output (paraphrased): This is one of the better use cases. EOB documents are formulaic and the model reads them accurately. It correctly parsed a remittance with a $250 deductible application and a $30 co-pay. It flagged that the patient could appeal the claim but didn’t know the specific appeal deadline — because that wasn’t in the text I pasted.

Tip: Include the full EOB, not a screenshot description. If your client sends you a photo of the EOB, use a PDF converter first. The model needs actual text.

Prompt 8: Renewal Review Summary

When to use it: Preparing for a renewal conversation. You want a quick reference on what changed year over year.

The prompt:

Below are coverage summaries from the prior policy year and the renewal offer for the same account. Create a side-by-side comparison showing: (1) any coverage that was reduced, (2) any coverage that was increased, (3) any new exclusions added, (4) any endorsements added or removed. Highlight changes in bold. If nothing changed in a category, write "No change."

Prior year:
[Paste prior coverage summary]

Renewal:
[Paste renewal coverage summary]

Example output (paraphrased): The comparison caught a reduction in loss of use coverage from 30% to 20% of dwelling value, which I had missed scanning the documents. It also flagged the removal of a scheduled personal property endorsement. It did not flag a change in the deductible structure from flat to percentage-based for wind/hail — I had described the deductible as “$2,500 wind/hail” in both pastes without specifying the calculation method, so from the model’s perspective nothing changed.

Tip: Be explicit about deductible types. Flat vs. percentage deductibles look similar when pasted as raw text and the difference matters a lot when the client files a claim on a $400,000 home.

Common Mistakes When Prompting for Policy Summaries

Pasting too little context. The model only knows what you give it. Missing endorsements, missing riders, missing declarations pages all produce incomplete summaries.

Asking for interpretation, not extraction. “Is this a good policy?” is not a useful prompt. “What are the coverage limits for each category?” produces something you can actually use.

Trusting numbers without verification. ChatGPT will occasionally transpose limits (stating $300,000 when the policy shows $300K per occurrence, $600K aggregate) or summarize sublimits incorrectly. Every number that goes to a client needs a human check.

Skipping the business type context. For commercial policies especially, telling the model what kind of business you’re working with dramatically improves gap analysis accuracy.

Using the output as a legal document. Summaries are for internal use and client communication. The actual policy language controls. Make sure clients know this.

What These Prompts Won’t Do

They won’t tell you if a policy is competitively priced. They won’t identify subtle coverage traps that require underwriting experience to recognize. They won’t replace knowing your product. They handle the mechanical work of extracting and formatting information — which frees up time for the judgment calls that actually require expertise.

Used carefully, that’s enough to make a meaningful difference in how much time you spend on routine documentation.