Hands-on use

Choose a model class before you build

Providers give their models different names, but most offer the same tradeoff: a fast economical tier, a balanced reasoning tier, and a frontier tier. Choose by the task's quality bar, complexity, latency, and volume. The model-selection guidance from Anthropic uses the same capability, speed, and cost framework.

Named examples verified August 2026 against each provider's own model documentation (Anthropic, OpenAI, Google). Two notes on reading the table: Anthropic sells Claude Fable 5 above Opus 5 as a premium frontier option, and Google's newer Pro models were still in preview or partner testing at that date, so the generally available Pro flagship sits a generation behind its Flash line. Lineups change every few months; the tiers are stable, the names are not.

Route by step, not by workflow

A submission workflow might use a fast model to classify attachments, a balanced model to create the underwriter brief, and a frontier model only when documents conflict or the case needs deeper analysis. The model router should escalate on low confidence, failed validation, or a task outside the evaluated pattern, not on the basis of prose that merely sounds uncertain.

Non-negotiable rule: model tier does not change authority. A frontier model may be more capable, but it does not get to quote, bind, set reserves, change a rate, or file a document without the same permissions, deterministic controls, and human gate. For the implementation pattern, see the practice ladder's harness section.

These categories are provider-neutral, and the named examples above are a dated snapshot rather than a recommendation. The capability, speed, and cost framework is reflected in Anthropic's model-selection guidance; use each provider's current documentation and your own evaluations to select the actual model.

Anatomy of a good prompt

Five ingredients, most of which people skip:

Before: "Summarize this loss run."

After: "You are a commercial lines underwriter preparing for renewal. Summarize the attached 5-year loss run for a $2M GL account: total incurred by year, the top 3 claims with one-line descriptions, frequency and severity trends, and any open large losses. Format as a short brief I can paste into the underwriting file. Flag data gaps rather than guessing."

Same document, radically different output.

Ten techniques that do most of the work

1. Paste the source material. Never ask the model to recall a policy form, regulation, or account from memory; give it the document and ask it to work from that. This is the difference between grounded output and confident fiction.
2. Assign a role. "You are a skeptical reinsurance treaty reviewer" produces different (better) output than no framing.
3. Specify the output format. Table vs. prose vs. bullet brief; word limits; audience ("explain for a board member" vs. "for an actuary").
4. Show an example. If you want a specific style (a triage note, a file summary format), paste one good example and say "match this format." The highest-leverage trick almost nobody uses.
5. Ask for reasoning first on hard problems. "Think through the coverage question step by step before giving your conclusion." For genuinely hard analysis, use the tool's extended-thinking mode if it has one.
6. Iterate. The first output is a draft, not a verdict. "Shorter." "More formal." "You missed the 2023 claim; redo with that included." Follow-ups are cheap.
7. Use it as a critic, not just a drafter. Paste your own memo or analysis and ask for a skeptical peer review. One of the highest-value, lowest-risk uses that exists.
8. Break big tasks into steps. "First extract the claims, then we'll analyze trends" beats one giant do-everything prompt: you catch errors at each stage.
9. Tell it what to do when unsure. Add "If the information isn't in the document, say 'not stated'; do not guess." This one sentence dramatically cuts fabrication in extraction tasks.
10. Start fresh chats for new topics. Long conversations accumulate stale context and the model starts blending earlier instructions into new answers.

Copy-paste templates, insurance flavored

Examples use P&C documents; adapt the nouns to your line of business. The structure is what matters.

Summarize a claim file

You are a senior commercial claims adjuster. Below is a claim file. Summarize it as: (1) a 3-sentence overview, (2) coverage issues, (3) current reserves vs. realistic exposure, (4) open questions, (5) recommended next actions. Flag anything unusual. If something isn't in the file, say so rather than assuming.

[paste file]

Extract a loss run to a table

Extract every claim from the loss run below into a table with columns: claim number, loss date, line of business, cause of loss, status, paid, incurred. Use "not stated" for missing values; do not guess or infer. After the table, list any entries you were uncertain about and why.

[paste loss run]

Compare policy wordings

Compare the two endorsement wordings below. List every substantive difference in coverage grants, exclusions, conditions, and definitions in a table. Ignore formatting and ordering differences. Then explain which differences would matter most for [describe the risk] and why.

[paste wording A] / [paste wording B]

Draft a communication

Draft an email to a retail broker declining the [risk type] submission for [named insured] because [reasons]. Professional and warm, under 150 words, keep the door open for other business, and do not promise to reconsider this risk.

Red-team your own work

Below is my draft analysis. Act as a skeptical peer reviewer: list the strongest objections, the weakest assumptions, anything missing, and alternative interpretations of the data. Rank by importance. Do not compliment the work.

[paste analysis]

Understand or write code (actuaries/analysts)

Explain what this [R/Python/SQL/VBA] code does, section by section, then flag any bugs, silent assumptions, or edge cases that could produce wrong numbers.

[paste code]

Turn a meeting into actions

From the meeting notes below, produce: decisions made, action items (owner + due date if stated), and open questions. Don't invent owners or dates that weren't stated.

[paste notes]

Habits of effective users

• Verify anything that leaves your hands: numbers, quotes, citations, policy-language references. LLMs fabricate plausible-looking citations; check every one against the source.
• Numbers → code, not mental math. For anything quantitative, ask the model to write the calculation (Excel formula, Python, SQL) rather than compute it in prose. The code is checkable; the mental math often isn't right.
• Drafts, not decisions. Great for the first 80%; you own the last 20% and the signature.
• Re-ask important questions in a fresh chat. If the answer changes materially, the model is guessing; dig deeper before relying on it.
• Build a prompt library. When a prompt works well, save it. Teams that share proven prompts get consistent quality; individuals reinventing prompts get lottery results.
• Give it your standards. Paste your team's checklist, style guide, or a model example of "good"; the model will hold work to it.
• Use it to learn. "Explain ceded reinsurance accounting like I'm a new analyst, then quiz me" is a legitimately excellent use with essentially zero risk.
• Respect the data rules. Sanctioned tools only; no client or confidential data in personal accounts. No exceptions. See the policy template in Governance.

Common mistakes and their fixes