Review driver hours-of-service logs for violations and fatigue risk
Finds hours-of-service violations, risky patterns, and dispatch causes in driver logs, with coaching and process fixes.
The prompt
Review my drivers' hours-of-service data and tell me what's going wrong.
Rules we operate under: [e.g. "US FMCSA property-carrying 11/14 with 60/70-hour limits", "EU drivers' hours", "Canada cycle 1", short-haul exemption yes/no]
Log data: [PASTE ELD EXPORT OR SUMMARY - driver, date, on-duty, driving, off-duty, sleeper berth, violations flagged]
Period covered: [DATES]
Type of work: [long haul, regional, local, team drivers]
What I suspect: [e.g. "one terminal keeps running out of hours", "too many edits"]
Analyze:
1. Violations by type (driving limit, duty window, break, cycle, form and manner) and by driver, terminal, and dispatcher.
2. Risky patterns that are legal but tiring: back-to-back maximum days, overnight driving swings, short resets, frequent split-sleeper use.
3. Red flags for log falsification: personal conveyance overuse, unassigned driving, edits after the fact, gaps that do not match fuel or GPS data.
4. Root causes on our side: unrealistic appointment times, detention at shippers, poor load planning, pay structure pushing drivers.
Return:
- A summary table: Issue | Count | Drivers or lanes affected | Likely cause.
- Which drivers need coaching versus which problems are dispatch or customer problems.
- 5 process changes to cut violations next month.
- A short, non-accusatory script for a coaching conversation with a driver.
Only use numbers from my data. Flag where you need more data as [ADD]. This is general guidance; confirm rule interpretations with a compliance specialist.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Legal & Compliance prompts
What to fill in
- DATES
- long haul, regional, local, team drivers
- ADD
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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