Advanced Prompting for Complex Coverage Analysis
Coverage analysis is the most technically demanding writing in insurance. It is where policy language, factual record, statute, and case law collide. Done well, it ends a dispute; done poorly, it starts one. AI is good at the structural and analytical heavy lifting; the licensed professional is good at the judgment. This lesson teaches you how to combine them.
What You'll Learn
- Advanced prompting techniques: chain-of-thought, role stacking, few-shot examples
- A structured coverage analysis prompt template
- How to walk AI through layered policy language (declarations, base form, endorsements, exclusions, conditions)
- Where coverage analysis ends and legal advice begins
A Quick Refresher on Coverage Analysis
A coverage analysis answers four questions:
- Is the loss within an insuring agreement? (Coverage A property, Coverage B liability, etc.)
- Does any exclusion apply? (Pollution, mold, intentional acts, etc.)
- Does any exception to an exclusion bring it back in? (Sudden and accidental, etc.)
- Are conditions and warranties satisfied? (Notice, cooperation, suit limitation, etc.)
These are sequential. You do not ask "does the exclusion apply?" before confirming the loss is within the insuring agreement. AI can help work through each step in order.
Advanced Prompting Technique 1: Chain-of-Thought
Most AI tools produce better answers when asked to "think step by step." For coverage, you can structure that reasoning explicitly.
You are a senior coverage attorney. You will analyze a
coverage question. Walk through these steps in order. For
each step, write your reasoning before stating your
conclusion. Do not skip steps.
Step 1: Identify the operative policy form and effective
date.
Step 2: Identify the insuring agreement that would respond
to this loss.
Step 3: Determine whether the facts trigger that insuring
agreement.
Step 4: Walk through every potentially applicable exclusion.
For each, state whether it applies and why.
Step 5: For any applicable exclusion, identify any
exception that may bring coverage back.
Step 6: Walk through the relevant conditions (notice,
cooperation, suit limitation, etc.). State whether each
appears satisfied.
Step 7: State your overall coverage conclusion (Coverage
likely, Coverage unlikely, or Issues require investigation).
Step 8: List the open items that would change your
conclusion.
Constraint: Use only the policy text and facts I provide.
Do not cite cases or statutes you cannot verify. Mark any
factual gap as "OPEN ITEM".
Policy text: [paste relevant sections]
Facts: [paste]
This produces a coverage analysis structure that mirrors how a coverage attorney would actually work.
Advanced Prompting Technique 2: Role Stacking
Sometimes you want the AI to take multiple perspectives on the same issue.
You will adopt three roles in sequence. For each role,
produce a one-paragraph analysis of the coverage issue
below.
ROLE 1: Plaintiff coverage attorney arguing FOR coverage.
ROLE 2: Defense coverage attorney arguing AGAINST coverage.
ROLE 3: Neutral coverage attorney providing the most
likely answer with reasoning.
Issue: [describe coverage question]
Policy text: [paste]
Facts: [paste]
This is excellent for testing the strength of a coverage position before you write a denial or accept a tender.
Advanced Prompting Technique 3: Few-Shot Examples
Show the AI two or three example outputs in the format you want, then ask it to apply the same format to a new case.
You are a coverage analyst. Below are two example coverage
analyses. Produce a third in the same format for the new
fact pattern.
EXAMPLE 1:
[paste a complete prior coverage analysis you wrote]
EXAMPLE 2:
[paste another]
NEW CASE:
Policy text: [paste]
Facts: [paste]
Format your output identically to the examples.
Few-shot prompting dramatically improves output consistency and is especially useful for templated coverage opinions.
Working with Layered Policy Language
Real coverage questions rarely involve just one document. You typically have:
- The declarations page
- The base policy form (e.g., CG 00 01)
- One or more endorsements modifying the base form
- Schedules of additional insureds, locations, or operations
- State-mandated amendatory endorsements
A useful prompt structure:
You are a senior coverage analyst. The full operative
policy is composed of:
DECLARATIONS: [paste]
BASE FORM: [paste — or describe form number and version
if Claude/ChatGPT cannot reference it directly]
ENDORSEMENTS: list each by form number, then paste text
For the coverage question below, walk through:
1. The base form's treatment of the issue
2. Whether any endorsement modifies that treatment
3. The final, post-endorsement coverage statement
4. Any open items
Coverage question: [describe]
Facts: [paste]
Constraint: Do not invent any policy text not provided.
Do not cite case law.
When to Bring AI into the Analysis vs. Out
AI is appropriate for:
- Initial structural analysis ("walk through the steps")
- Drafting opinion letters and ROR letters from your conclusions
- Comparing two coverage positions
- Generating questions you should investigate
- Explaining policy language in plain English
AI is NOT appropriate for:
- The final coverage decision in disputed claims
- Citing case law (it will hallucinate citations)
- Replacing licensed counsel on bad-faith exposure
- Producing the actual denial language without licensed review
Coverage Opinion Letter Drafting
After your analysis, AI can draft the opinion letter.
You are a senior coverage analyst. Based on the analysis
below, draft a coverage opinion letter to the claims
director. Sections:
- Question presented (one sentence)
- Short answer (one sentence)
- Operative policy and form
- Material facts (bullet list)
- Analysis (apply the law to the facts)
- Conclusion
- Open items and recommended next steps
Tone: professional, clear, no hedging beyond what is
warranted by open items. Length: 700-900 words.
Constraints: Use only the analysis I provide. Do not
introduce new arguments. Do not cite cases I have not
provided. Do not invent statutes.
Analysis: [paste your prior chain-of-thought output]
A Note on Bad-Faith Exposure
Bad-faith claims are now a leading source of insurer losses in many states. Plaintiff attorneys frequently subpoena AI tools and prompts in discovery. Treat every coverage prompt as if it might appear in a deposition exhibit.
Practical implications:
- Document which AI tool, version, and prompt produced the output
- Save inputs and outputs together
- Have licensed coverage counsel review any AI-generated coverage opinion before action
- Never use AI to look for ways to deny coverage — use it to look for the correct answer
Key Takeaways
- Chain-of-thought, role stacking, and few-shot examples are advanced techniques that meaningfully improve coverage analysis output.
- A structured 8-step coverage analysis prompt mirrors how a coverage attorney works.
- Layered policy language (declarations + base form + endorsements) requires a structured prompt that walks through each layer in order.
- Coverage analysis is one of the highest-stakes places to use AI. Document everything, and have licensed counsel review before action.