Review a pre-bill before it goes to the client
A billing partner's pre-bill review that catches write-down risks, guideline violations, and errors, and estimates realization before invoices go out.
The prompt
Review this pre-bill for me before I finalize the invoice.
Pre-bill entries: [PASTE - date, timekeeper, rate, hours, narrative, costs]
Client's outside counsel guidelines or rate agreement: [PASTE KEY TERMS - or "none"]
Budget or fee estimate given to the client: [AMOUNT AND SCOPE]
Billed to date before this invoice: [AMOUNT]
Relationship context: [e.g. "client complained about last bill", "new client"]
Check for:
1. Rate errors: rates that differ from the engagement letter or agreed rate card.
2. Guideline violations: block billing, minimum increments, staffing caps, unapproved timekeepers, billed internal conferences, overhead charged as costs (e.g. routine copying, Westlaw if excluded).
3. Duplication: same task by multiple timekeepers, repeated "review file" entries, training time for new team members.
4. Vague or risky narratives that invite challenge or reveal strategy.
5. Work outside the engagement scope.
6. Budget status: percent of estimate used vs. percent of work done, and whether the client must be told about an overrun.
Return:
- A table of flagged entries: entry, issue, suggested fix (edit, write down, move, or no change), and dollar impact.
- Totals: standard value, suggested write-downs, expected billed value, and realization percentage.
- A two-to-three sentence cover note to the client if the bill exceeds the estimate.
Do not change numbers silently; show every calculation. Final billing judgment is the responsible attorney's.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Finance & Accounting prompts
What to fill in
- PASTE - date, timekeeper, rate, hours, narrative, costs
- PASTE KEY TERMS - or "none"
- AMOUNT AND SCOPE
- AMOUNT
- e.g. "client complained about last bill", "new client"
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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