ThinkWithPrompt by SynotticThinkWithPrompt

Brief my teaching assistants and align their grading for the term

A TA onboarding guide covering roles, communication, grading norms, a calibration session, and escalation rules.

Learning & TeachingFew-ShotStandardEducationExplain & TeachFill-in Template

The prompt

Help me prepare my teaching assistants for the term. Course: [TITLE, ENROLLMENT, FORMAT] TA team: [number, experience level, hours per week] What TAs will do: [e.g. "lead weekly sections, grade problem sets and lab reports, hold office hours"] Grading materials: [PASTE RUBRICS OR DESCRIBE] Communication tools: [LMS, forum, email, chat] Problems from past terms: [e.g. "inconsistent grading between sections", "TAs extending deadlines on their own"] Create: 1. A TA guide: roles and responsibilities, weekly time expectations, what TAs can decide on their own vs. must send to me (extensions, regrades, integrity concerns, accommodations, student distress). 2. Grading norms: how to apply each rubric criterion, partial-credit rules, feedback expectations (length, tone, at least one specific next step), and turnaround times. 3. A 45-minute calibration session plan: everyone grades the same 3 sample responses, compares scores, discusses differences, and agrees on decision rules. Include what to record. 4. A weekly check-in agenda (15 minutes). 5. A student-wellbeing note: how TAs should respond and refer if a student discloses distress or a crisis, using our campus resources [ADD CAMPUS RESOURCES]. Keep it practical and short enough that TAs will read it. Remind TAs about student privacy rules for grades and records.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • TITLE, ENROLLMENT, FORMAT
  • number, experience level, hours per week
  • PASTE RUBRICS OR DESCRIBE
  • LMS, forum, email, chat
  • ADD CAMPUS RESOURCES

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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